DOMAIN:
Semiconductor manufacturing process
• CONTEXT:
A complex modern semiconductor manufacturing process is normally under constant surveillance via the monitoring of signals/ variables collected from sensors and or process measurement points. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be applied to identify the most relevant signals. The Process Engineers may then use these signals to determine key factors contributing to yield excursions downstream in the process. This will enable an increase in process throughput, decreased time to learning and reduce the per unit production costs. These signals can be used as features to predict the yield type. And by analysing and trying out different combinations of features, essential signals that are impacting the yield type can be identified.
• DATA DESCRIPTION:
sensor-data.csv : (1567, 592) The data consists of 1567 examples each with 591 features. The dataset presented in this case represents a selection of such features where each example represents a single production entity with associated measured features and the labels represent a simple pass/fail yield for in house line testing. Target column “ –1” corresponds to a pass and “1” corresponds to a fail and the data time stamp is for that specific test point.
• PROJECT OBJECTIVE:
We will build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.

#import libraries that will be used for EDA
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.stats import zscore
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn import preprocessing
from sklearn.metrics import average_precision_score, confusion_matrix, accuracy_score, classification_report, plot_confusion_matrix
import warnings
warnings.filterwarnings('ignore')
from sklearn.decomposition import PCA
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

Data = pd.read_csv('signal-data.csv')

Data.shape
# We can see that we have huge number of features

(1567, 592)

Data.head()
# We can see that datset contains null values, also we have columns with same value

Data.isnull().sum().sum()
# we can see that total of 41951 data is missing in entire data set

41951

Data.info()
# We can see that we have float64(590), int64(1), object(1) data types in our data set

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1567 entries, 0 to 1566
Columns: 592 entries, Time to Pass/Fail
dtypes: float64(590), int64(1), object(1)
memory usage: 7.1+ MB

Data.isnull().sum()

Time            0
0               6
1               7
2              14
3              14
4              14
5              14
6              14
7               9
8               2
9               2
10              2
11              2
12              2
13              3
14              3
15              3
16              3
17              3
18              3
19             10
20              0
21              2
22              2
23              2
24              2
25              2
26              2
27              2
28              2
29              2
30              2
31              2
32              1
33              1
34              1
35              1
36              1
37              1
38              1
39              1
40             24
41             24
42              1
43              1
44              1
45              1
46              1
47              1
48              1
49              1
50              1
51              1
52              1
53              4
54              4
55              4
56              4
57              4
58              4
59              7
60              6
61              6
62              6
63              7
64              7
65              7
66              6
67              6
68              6
69              6
70              6
71              6
72            794
73            794
74              6
75             24
76             24
77             24
78             24
79             24
80             24
81             24
82             24
83              1
84             12
85           1341
86              0
87              0
88              0
89             51
90             51
91              6
92              2
93              2
94              6
95              6
96              6
97              6
98              6
99              6
100             6
101             6
102             6
103             2
104             2
105             6
106             6
107             6
108             6
109          1018
110          1018
111          1018
112           715
113             0
114             0
115             0
116             0
117             0
118            24
119             0
120             0
121             9
122             9
123             9
124             9
125             9
126             9
127             9
128             9
129             9
130             9
131             9
132             8
133             8
134             8
135             5
136             6
137             7
138            14
139            14
140            14
141            14
142            14
143             9
144             2
145             2
146             2
147             2
148             2
149             3
150             3
151             3
152             3
153             3
154             3
155            10
156             0
157          1429
158          1429
159             2
160             2
161             2
162             2
163             2
164             2
165             2
166             2
167             2
168             2
169             2
170             1
171             1
172             1
173             1
174             1
175             1
176             1
177             1
178            24
179             1
180             1
181             1
182             1
183             1
184             1
185             1
186             1
187             1
188             1
189             1
190             4
191             4
192             4
193             4
194             4
195             4
196             7
197             6
198             6
199             6
200             7
201             7
202             7
203             6
204             6
205             6
206             6
207             6
208             6
209             6
210            24
211            24
212            24
213            24
214            24
215            24
216            24
217            24
218             1
219            12
220          1341
221             0
222             0
223             0
224            51
225            51
226             6
227             2
228             2
229             6
230             6
231             6
232             6
233             6
234             6
235             6
236             6
237             6
238             2
239             2
240             6
241             6
242             6
243             6
244          1018
245          1018
246          1018
247           715
248             0
249             0
250             0
251             0
252             0
253            24
254             0
255             0
256             9
257             9
258             9
259             9
260             9
261             9
262             9
263             9
264             9
265             9
266             9
267             8
268             8
269             8
270             5
271             6
272             7
273            14
274            14
275            14
276            14
277            14
278             9
279             2
280             2
281             2
282             2
283             2
284             3
285             3
286             3
287             3
288             3
289             3
290            10
291             0
292          1429
293          1429
294             2
295             2
296             2
297             2
298             2
299             2
300             2
301             2
302             2
303             2
304             2
305             1
306             1
307             1
308             1
309             1
310             1
311             1
312             1
313            24
314            24
315             1
316             1
317             1
318             1
319             1
320             1
321             1
322             1
323             1
324             1
325             1
326             4
327             4
328             4
329             4
330             4
331             4
332             7
333             6
334             6
335             6
336             7
337             7
338             7
339             6
340             6
341             6
342             6
343             6
344             6
345           794
346           794
347             6
348            24
349            24
350            24
351            24
352            24
353            24
354            24
355            24
356             1
357            12
358          1341
359             0
360             0
361             0
362            51
363            51
364             6
365             2
366             2
367             6
368             6
369             6
370             6
371             6
372             6
373             6
374             6
375             6
376             2
377             2
378             6
379             6
380             6
381             6
382          1018
383          1018
384          1018
385           715
386             0
387             0
388             0
389             0
390             0
391            24
392             0
393             0
394             9
395             9
396             9
397             9
398             9
399             9
400             9
401             9
402             9
403             9
404             9
405             8
406             8
407             8
408             5
409             6
410             7
411            14
412            14
413            14
414            14
415            14
416             9
417             2
418             2
419             2
420             2
421             2
422             3
423             3
424             3
425             3
426             3
427             3
428            10
429             0
430             2
431             2
432             2
433             2
434             2
435             2
436             2
437             2
438             2
439             2
440             2
441             1
442             1
443             1
444             1
445             1
446             1
447             1
448             1
449            24
450            24
451             1
452             1
453             1
454             1
455             1
456             1
457             1
458             1
459             1
460             1
461             1
462             4
463             4
464             4
465             4
466             4
467             4
468             7
469             6
470             6
471             6
472             7
473             7
474             7
475             6
476             6
477             6
478             6
479             6
480             6
481             6
482            24
483            24
484            24
485            24
486            24
487            24
488            24
489            24
490             1
491            12
492          1341
493             0
494             0
495             0
496            51
497            51
498             6
499             2
500             2
501             6
502             6
503             6
504             6
505             6
506             6
507             6
508             6
509             6
510             2
511             2
512             6
513             6
514             6
515             6
516          1018
517          1018
518          1018
519           715
520             0
521             0
522             0
523             0
524             0
525            24
526             0
527             0
528             9
529             9
530             9
531             9
532             9
533             9
534             9
535             9
536             9
537             9
538             9
539             8
540             8
541             8
542             2
543             2
544             2
545             2
546           260
547           260
548           260
549           260
550           260
551           260
552           260
553           260
554           260
555           260
556           260
557           260
558             1
559             1
560             1
561             1
562           273
563           273
564           273
565           273
566           273
567           273
568           273
569           273
570             0
571             0
572             0
573             0
574             0
575             0
576             0
577             0
578           949
579           949
580           949
581           949
582             1
583             1
584             1
585             1
586             1
587             1
588             1
589             1
Pass/Fail       0
dtype: int64

# We can see that many columns have too many null values, so columns which have null values more than 70% can be droppped
# as majority of the data is missing imputing and creating majority of the artificial data is of no use in above case
# SO columns having null values more than roughly 1000 can be dropped
counter  = 0

for columns in Data.columns:
    if Data[columns].isnull().sum() >= 1000:
        counter = counter + 1
        print( columns , 'Column has', Data[columns].isnull().sum() , ' null values, hence can be dropped')
        del Data[columns]
        

print('New shape of the data set after deleting above', counter ,' columns is', Data.shape)

85 Column has 1341  null values, hence can be dropped
109 Column has 1018  null values, hence can be dropped
110 Column has 1018  null values, hence can be dropped
111 Column has 1018  null values, hence can be dropped
157 Column has 1429  null values, hence can be dropped
158 Column has 1429  null values, hence can be dropped
220 Column has 1341  null values, hence can be dropped
244 Column has 1018  null values, hence can be dropped
245 Column has 1018  null values, hence can be dropped
246 Column has 1018  null values, hence can be dropped
292 Column has 1429  null values, hence can be dropped
293 Column has 1429  null values, hence can be dropped
358 Column has 1341  null values, hence can be dropped
382 Column has 1018  null values, hence can be dropped
383 Column has 1018  null values, hence can be dropped
384 Column has 1018  null values, hence can be dropped
492 Column has 1341  null values, hence can be dropped
516 Column has 1018  null values, hence can be dropped
517 Column has 1018  null values, hence can be dropped
518 Column has 1018  null values, hence can be dropped
New shape of the data set after deleting above 20  columns is (1567, 572)

# we wont be requiring time column to classify condutor into pass or fail category, hence we can drop time column too
Data.drop(labels = 'Time', axis = 1, inplace = True)
Data.shape

(1567, 571)

Data.describe().T

# We can see that manu columns have same values across all rows, such columns wont support in creating our model, 
# hence such columns can be dropped too
counter  = 0
for columns in Data.columns:
    if Data[columns].min() == Data[columns].median() and Data[columns].max() == Data[columns].median():
        counter = counter + 1
        print( columns , 'Column has single value ', Data[columns].median(), ', hence can be dropped')
        del Data[columns]
        

print('New shape of the data set after deleting above', counter ,' columns is', Data.shape)

5 Column has single value  100.0 , hence can be dropped
13 Column has single value  0.0 , hence can be dropped
42 Column has single value  70.0 , hence can be dropped
49 Column has single value  1.0 , hence can be dropped
52 Column has single value  0.0 , hence can be dropped
69 Column has single value  1.0 , hence can be dropped
97 Column has single value  0.0 , hence can be dropped
141 Column has single value  0.0 , hence can be dropped
149 Column has single value  0.0 , hence can be dropped
178 Column has single value  0.0 , hence can be dropped
179 Column has single value  0.0 , hence can be dropped
186 Column has single value  0.0 , hence can be dropped
189 Column has single value  0.0 , hence can be dropped
190 Column has single value  0.0 , hence can be dropped
191 Column has single value  0.0 , hence can be dropped
192 Column has single value  0.0 , hence can be dropped
193 Column has single value  0.0 , hence can be dropped
194 Column has single value  0.0 , hence can be dropped
226 Column has single value  0.0 , hence can be dropped
229 Column has single value  0.0 , hence can be dropped
230 Column has single value  0.0 , hence can be dropped
231 Column has single value  0.0 , hence can be dropped
232 Column has single value  0.0 , hence can be dropped
233 Column has single value  0.0 , hence can be dropped
234 Column has single value  0.0 , hence can be dropped
235 Column has single value  0.0 , hence can be dropped
236 Column has single value  0.0 , hence can be dropped
237 Column has single value  0.0 , hence can be dropped
240 Column has single value  0.0 , hence can be dropped
241 Column has single value  0.0 , hence can be dropped
242 Column has single value  0.0 , hence can be dropped
243 Column has single value  0.0 , hence can be dropped
256 Column has single value  0.0 , hence can be dropped
257 Column has single value  0.0 , hence can be dropped
258 Column has single value  0.0 , hence can be dropped
259 Column has single value  0.0 , hence can be dropped
260 Column has single value  0.0 , hence can be dropped
261 Column has single value  0.0 , hence can be dropped
262 Column has single value  0.0 , hence can be dropped
263 Column has single value  0.0 , hence can be dropped
264 Column has single value  0.0 , hence can be dropped
265 Column has single value  0.0 , hence can be dropped
266 Column has single value  0.0 , hence can be dropped
276 Column has single value  0.0 , hence can be dropped
284 Column has single value  0.0 , hence can be dropped
313 Column has single value  0.0 , hence can be dropped
314 Column has single value  0.0 , hence can be dropped
315 Column has single value  0.0 , hence can be dropped
322 Column has single value  0.0 , hence can be dropped
325 Column has single value  0.0 , hence can be dropped
326 Column has single value  0.0 , hence can be dropped
327 Column has single value  0.0 , hence can be dropped
328 Column has single value  0.0 , hence can be dropped
329 Column has single value  0.0 , hence can be dropped
330 Column has single value  0.0 , hence can be dropped
364 Column has single value  0.0 , hence can be dropped
369 Column has single value  0.0 , hence can be dropped
370 Column has single value  0.0 , hence can be dropped
371 Column has single value  0.0 , hence can be dropped
372 Column has single value  0.0 , hence can be dropped
373 Column has single value  0.0 , hence can be dropped
374 Column has single value  0.0 , hence can be dropped
375 Column has single value  0.0 , hence can be dropped
378 Column has single value  0.0 , hence can be dropped
379 Column has single value  0.0 , hence can be dropped
380 Column has single value  0.0 , hence can be dropped
381 Column has single value  0.0 , hence can be dropped
394 Column has single value  0.0 , hence can be dropped
395 Column has single value  0.0 , hence can be dropped
396 Column has single value  0.0 , hence can be dropped
397 Column has single value  0.0 , hence can be dropped
398 Column has single value  0.0 , hence can be dropped
399 Column has single value  0.0 , hence can be dropped
400 Column has single value  0.0 , hence can be dropped
401 Column has single value  0.0 , hence can be dropped
402 Column has single value  0.0 , hence can be dropped
403 Column has single value  0.0 , hence can be dropped
404 Column has single value  0.0 , hence can be dropped
414 Column has single value  0.0 , hence can be dropped
422 Column has single value  0.0 , hence can be dropped
449 Column has single value  0.0 , hence can be dropped
450 Column has single value  0.0 , hence can be dropped
451 Column has single value  0.0 , hence can be dropped
458 Column has single value  0.0 , hence can be dropped
461 Column has single value  0.0 , hence can be dropped
462 Column has single value  0.0 , hence can be dropped
463 Column has single value  0.0 , hence can be dropped
464 Column has single value  0.0 , hence can be dropped
465 Column has single value  0.0 , hence can be dropped
466 Column has single value  0.0 , hence can be dropped
481 Column has single value  0.0 , hence can be dropped
498 Column has single value  0.0 , hence can be dropped
501 Column has single value  0.0 , hence can be dropped
502 Column has single value  0.0 , hence can be dropped
503 Column has single value  0.0 , hence can be dropped
504 Column has single value  0.0 , hence can be dropped
505 Column has single value  0.0 , hence can be dropped
506 Column has single value  0.0 , hence can be dropped
507 Column has single value  0.0 , hence can be dropped
508 Column has single value  0.0 , hence can be dropped
509 Column has single value  0.0 , hence can be dropped
512 Column has single value  0.0 , hence can be dropped
513 Column has single value  0.0 , hence can be dropped
514 Column has single value  0.0 , hence can be dropped
515 Column has single value  0.0 , hence can be dropped
528 Column has single value  0.0 , hence can be dropped
529 Column has single value  0.0 , hence can be dropped
530 Column has single value  0.0 , hence can be dropped
531 Column has single value  0.0 , hence can be dropped
532 Column has single value  0.0 , hence can be dropped
533 Column has single value  0.0 , hence can be dropped
534 Column has single value  0.0 , hence can be dropped
535 Column has single value  0.0 , hence can be dropped
536 Column has single value  0.0 , hence can be dropped
537 Column has single value  0.0 , hence can be dropped
538 Column has single value  0.0 , hence can be dropped
New shape of the data set after deleting above 116  columns is (1567, 455)

#Lets see if we have features with very less variance, if we have any then we can drop those features too, 
# since column haveing very low variance won't be helpfull in building our model 
Data.var(axis = 0, skipna = True)

0            5.420167e+03
1            6.465399e+03
2            8.710261e+02
3            1.950915e+05
4            3.175947e+03
6            3.890284e+01
7            8.030522e-05
8            5.460702e-03
9            2.284992e-04
10           8.652916e-05
11           1.550536e-04
12           1.060984e+01
14           7.820947e+00
15           2.965661e+02
16           5.778575e+00
17           1.455018e-04
18           7.734187e+00
19           4.750885e-02
20           2.801249e-04
21           3.929060e+05
22           8.731938e+04
23           1.904848e+06
24           8.425610e+06
25           3.154193e-02
26           3.590837e-02
27           1.548156e+00
28           1.197977e+01
29           1.670310e-01
30           1.085303e-03
31           2.865695e-01
32           4.106902e+00
33           1.807563e+00
34           1.398586e+00
35           6.629330e+00
36           1.398587e+00
37           9.250163e-02
38           1.995906e-01
39           3.266047e+00
40           5.790252e+02
41           5.571606e+00
43           3.887156e+01
44           3.063816e-02
45           6.161069e+01
46           1.481166e+02
47           3.596216e-02
48           2.046885e+01
50           7.471848e+01
51           3.711869e+03
53           3.019461e-03
54           3.549857e-03
55           6.630273e+02
56           4.634017e-05
57           1.743858e-05
58           7.241121e-03
59           9.086321e+01
60           3.633544e+01
61           7.555730e-02
62           7.446001e+01
63           5.069245e+01
64           2.477518e+01
65           5.071865e+01
66           1.350960e+02
67           9.455766e+04
68           1.797841e+01
70           9.099615e+01
71           1.001843e+03
72           3.381362e+02
73           3.108129e+02
74           1.127625e-02
75           4.969407e-04
76           1.102458e-03
77           9.839660e-04
78           2.291757e-03
79           5.326701e-04
80           2.423201e-03
81           2.897187e-04
82           1.301356e-03
83           2.665155e-01
84           2.551565e-05
86           1.393668e-03
87           1.650670e-04
88           2.866238e+03
89           2.742917e-03
90           1.570645e+05
91           7.688300e-03
92           1.043952e-05
93           9.059099e-06
94           3.032293e-08
95           1.090102e-08
96           4.821456e-02
98           1.824228e-01
99           3.936357e-03
100          1.264063e-07
101          4.873603e-08
102          3.964927e-03
103          9.391460e-06
104          7.237052e-07
105          1.025491e-05
106          8.927623e-06
107          7.651836e-03
108          7.526910e-03
112          2.395073e-03
113          1.472066e-04
114          2.782538e-06
115          2.396029e+03
116          9.018359e-05
117          4.205749e+01
118          6.563458e-05
119          8.007676e-05
120          1.545158e-02
121          9.923691e-03
122          8.174338e-01
123          1.173213e-02
124          1.302878e-02
125          7.871093e-02
126          6.424745e-02
127          1.833556e-02
128          6.978819e-02
129          1.489568e+00
130          6.811409e-03
131          5.065094e-06
132          2.828198e-03
133          4.274154e+01
134          8.942947e+00
135          3.311384e+03
136          2.906266e+03
137          2.730378e+03
138          1.524079e+02
139          6.932721e+04
140          2.569700e+05
142          1.261851e+01
143          1.643035e-06
144          3.762937e-03
145          7.044026e-04
146          4.771816e-04
147          7.356522e-04
148          3.512113e+02
150          1.050954e+01
151          9.611576e+02
152          5.458794e+02
153          8.734168e-05
154          2.744942e+01
155          1.259842e+00
156          6.268600e-03
159          9.663736e+05
160          3.304049e+05
161          1.797120e+07
162          4.294927e+07
163          1.488131e-02
164          5.882256e-02
165          1.659172e-01
166          1.253853e+00
167          4.003940e-01
168          2.269439e-03
169          3.917138e-02
170          2.479616e-02
171          3.694800e-03
172          5.075566e-03
173          9.164992e-03
174          5.076119e-03
175          1.353084e-02
176          5.612675e-03
177          8.003409e-02
180          1.096691e+01
181          5.035615e-02
182          1.733932e+01
183          4.673228e+01
184          1.214354e-02
185          5.167770e+01
187          7.413058e+01
188          4.714056e+02
195          1.561727e-01
196          2.471475e+02
197          1.113481e+02
198          2.891269e-01
199          2.704563e+02
200          7.552859e+01
201          2.605587e+01
202          2.138293e+02
203          3.049144e+02
204          3.193394e+05
205          1.331966e+02
206          2.562460e-03
207          3.061645e+02
208          7.877645e+02
209          1.364396e+00
210          1.769425e-03
211          6.252572e-04
212          9.971476e-04
213          2.812135e-03
214          3.187243e-03
215          9.264001e-04
216          6.637996e-04
217          2.142159e-03
218          1.369921e+00
219          2.709667e-06
221          5.431336e-04
222          3.128922e-03
223          3.042185e+03
224          5.071041e-03
225          1.876363e+05
227          1.156854e-04
228          1.154524e-04
238          2.883027e-06
239          2.077649e-06
247          4.472961e-03
248          2.424092e-03
249          2.487177e-04
250          2.980862e+03
251          1.404188e-03
252          4.141535e+03
253          5.028854e-04
254          8.339595e-05
255          1.448031e-02
267          8.790437e-04
268          5.394028e+01
269          1.327862e+00
270          7.059382e+01
271          3.192096e+02
272          3.146194e+02
273          1.467244e+01
274          7.328693e+03
275          2.854390e+04
277          1.431462e+00
278          1.156231e-07
279          4.116502e-04
280          4.202943e-05
281          3.075236e-05
282          7.310877e-05
283          3.439029e+01
285          9.272202e-01
286          9.533236e+01
287          5.455806e+01
288          8.620656e-06
289          2.614667e+00
290          7.343397e-02
291          6.527423e-04
294          2.275768e+05
295          8.038966e+04
296          3.901065e+06
297          1.041304e+07
298          4.124834e-03
299          1.711528e-02
300          4.802548e-02
301          1.102118e-01
302          3.901178e-02
303          2.105579e-04
304          4.207780e-03
305          3.293324e-03
306          6.451579e-04
307          7.544716e-04
308          1.128504e-03
309          7.546067e-04
310          1.888758e-03
311          8.292196e-04
312          1.376298e-02
316          1.037605e+00
317          5.240654e-03
318          1.478486e+00
319          4.748297e+00
320          1.016640e-03
321          4.481666e+00
323          6.344652e+00
324          4.376947e+01
331          4.022668e-03
332          3.186857e+01
333          1.158345e+01
334          2.988838e-02
335          3.342642e+01
336          5.709511e+01
337          2.888079e+00
338          3.186627e+01
339          3.690200e+01
340          6.850702e+04
341          1.345350e+01
342          1.281152e-04
343          2.885650e+01
344          7.912496e+01
345          3.067040e+02
346          2.916409e+02
347          1.240352e-01
348          1.407185e-04
349          1.124280e-04
350          2.052244e-04
351          6.033369e-04
352          1.731107e-04
353          2.402275e-04
354          1.707700e-04
355          4.975885e-04
356          1.497052e-01
357          2.509253e-07
359          5.092760e-05
360          4.001028e-04
361          2.909175e+02
362          4.684651e-04
363          1.926598e+04
365          7.054390e-06
366          5.676064e-06
367          7.285036e-06
368          4.438111e-06
376          2.855300e-07
377          2.176596e-07
385          2.063713e-04
386          2.398823e-04
387          2.489346e-05
388          2.967696e+02
389          1.396180e-04
390          4.131632e+02
391          4.539671e-05
392          8.735296e-06
393          1.475212e-03
405          1.150958e-04
406          8.006542e+00
407          1.330142e-01
408          6.646691e+00
409          3.157172e+00
410          4.507035e+00
411          3.041238e-01
412          3.390615e+02
413          2.238091e+03
415          1.566870e+01
416          1.072122e+00
417          1.643909e+01
418          8.277387e+04
419          1.059167e+05
420          9.349482e+00
421          4.780139e+01
423          1.062560e+03
424          4.001024e+01
425          5.409213e+02
426          9.912593e-01
427          9.254643e+00
428          1.130549e+02
429          4.141425e+01
430          1.300330e+03
431          1.324626e+03
432          1.592359e+04
433          5.097610e+04
434          1.163414e+03
435          1.181282e+03
436          1.194242e+03
437          2.596203e+00
438          1.163359e+03
439          1.472731e+03
440          3.806237e+01
441          3.393432e-02
442          4.345375e-01
443          2.060719e-02
444          2.418700e-02
445          1.995218e-02
446          3.103171e-02
447          7.475464e-03
448          5.582603e-02
452          8.449220e-01
453          5.066121e+00
454          9.361522e+00
455          8.805422e-01
456          6.602986e+01
457          2.059105e+01
459          1.810576e+00
460          1.778273e+02
467          7.523348e+01
468          5.325337e+04
469          9.933116e+00
470          2.483394e+01
471          1.035127e+02
472          2.275103e+03
473          5.043215e+02
474          6.161773e+02
475          6.818230e+00
476          2.231913e+02
477          1.037348e+02
478          2.562460e+01
479          6.962254e+00
480          1.278241e+03
482          7.896736e+04
483          3.719668e+04
484          4.542296e+04
485          4.782532e+04
486          8.257811e+04
487          6.961030e+04
488          6.352605e+04
489          5.200530e+04
490          3.257524e+02
491          1.498868e+00
493          9.485753e-01
494          4.376087e+01
495          1.062773e+01
496          6.062225e+02
497          2.456835e+01
499          1.054764e+05
500          1.043312e+05
510          1.420667e+03
511          1.086788e+05
519          2.500959e+02
520          3.251698e+01
521          1.063435e+04
522          5.047300e+01
523          1.720243e+01
524          4.269767e+02
525          1.536930e+01
526          9.185833e-01
527          3.567179e+00
539          1.569791e+00
540          5.357191e-01
541          8.388993e+00
542          7.489852e-06
543          2.351759e-06
544          8.733216e-08
545          1.730657e+00
546          1.513722e-01
547          2.564295e+01
548          1.149565e+01
549          4.533945e-01
550          2.467063e+01
551          1.852640e+00
552          7.630337e-02
553          4.807702e+00
554          3.586237e-01
555          1.239572e+03
556          1.638422e+00
557          3.498521e+00
558          7.032422e-03
559          4.055869e-02
560          2.660272e-03
561          3.619918e+02
562          5.822583e+01
563          1.482495e-02
564          6.935761e+00
565          6.580836e-03
566          1.066596e+00
567          1.073280e-03
568          9.932996e-01
569          1.043114e+02
570          3.062408e+02
571          7.568653e-02
572          7.448498e+03
573          6.174138e-02
574          7.246945e+02
575          4.595636e-03
576          2.863327e+02
577          1.558819e+02
578          1.376002e-04
579          9.293216e-05
580          9.709368e-06
581          7.659920e+03
582          1.158943e-05
583          2.951497e-04
584          1.384171e-05
585          1.280232e+01
586          1.527245e-04
587          7.757268e-05
588          8.220099e-06
589          8.815693e+03
Pass/Fail    2.480144e-01
dtype: float64

#We can see that we have many features with very low variance,
#lets get rid of such features which has variance less than 10%
counter  = 0
for columns in Data.columns[:454]: # leaving the last column
    if Data[columns].var() <=10:
        counter = counter + 1
        print( columns , 'Column has variance of ', Data[columns].var(), 'which is very low, hence can be dropped')
        del Data[columns]
        

print('New shape of the data set after deleting above', counter ,' columns is', Data.shape)

7 Column has variance of  8.030522137796683e-05 which is very low, hence can be dropped
8 Column has variance of  0.0054607018028647765 which is very low, hence can be dropped
9 Column has variance of  0.00022849918565487037 which is very low, hence can be dropped
10 Column has variance of  8.652915872302494e-05 which is very low, hence can be dropped
11 Column has variance of  0.00015505359361185774 which is very low, hence can be dropped
14 Column has variance of  7.820946786353919 which is very low, hence can be dropped
16 Column has variance of  5.778574796383628 which is very low, hence can be dropped
17 Column has variance of  0.0001455017992932797 which is very low, hence can be dropped
18 Column has variance of  7.734187455719755 which is very low, hence can be dropped
19 Column has variance of  0.04750885460969863 which is very low, hence can be dropped
20 Column has variance of  0.00028012491013976854 which is very low, hence can be dropped
25 Column has variance of  0.03154192896995499 which is very low, hence can be dropped
26 Column has variance of  0.03590837324042573 which is very low, hence can be dropped
27 Column has variance of  1.5481562508658315 which is very low, hence can be dropped
29 Column has variance of  0.1670309511443572 which is very low, hence can be dropped
30 Column has variance of  0.0010853033811722226 which is very low, hence can be dropped
31 Column has variance of  0.2865694657314093 which is very low, hence can be dropped
32 Column has variance of  4.10690168344204 which is very low, hence can be dropped
33 Column has variance of  1.8075629222614 which is very low, hence can be dropped
34 Column has variance of  1.3985857497574437 which is very low, hence can be dropped
35 Column has variance of  6.629330325793535 which is very low, hence can be dropped
36 Column has variance of  1.3985874294421847 which is very low, hence can be dropped
37 Column has variance of  0.09250162542205558 which is very low, hence can be dropped
38 Column has variance of  0.1995905503449948 which is very low, hence can be dropped
39 Column has variance of  3.266047230814435 which is very low, hence can be dropped
41 Column has variance of  5.571606134393028 which is very low, hence can be dropped
44 Column has variance of  0.030638159733130922 which is very low, hence can be dropped
47 Column has variance of  0.03596215621214383 which is very low, hence can be dropped
53 Column has variance of  0.003019460758267987 which is very low, hence can be dropped
54 Column has variance of  0.0035498565580653093 which is very low, hence can be dropped
56 Column has variance of  4.634017080321758e-05 which is very low, hence can be dropped
57 Column has variance of  1.743857661527828e-05 which is very low, hence can be dropped
58 Column has variance of  0.007241120885653585 which is very low, hence can be dropped
61 Column has variance of  0.07555730046428144 which is very low, hence can be dropped
74 Column has variance of  0.011276246156309962 which is very low, hence can be dropped
75 Column has variance of  0.0004969407105601385 which is very low, hence can be dropped
76 Column has variance of  0.0011024577626164926 which is very low, hence can be dropped
77 Column has variance of  0.0009839660465110422 which is very low, hence can be dropped
78 Column has variance of  0.0022917572584526735 which is very low, hence can be dropped
79 Column has variance of  0.0005326701365019892 which is very low, hence can be dropped
80 Column has variance of  0.002423200925908655 which is very low, hence can be dropped
81 Column has variance of  0.00028971867724454105 which is very low, hence can be dropped
82 Column has variance of  0.001301355798026818 which is very low, hence can be dropped
83 Column has variance of  0.2665154925133077 which is very low, hence can be dropped
84 Column has variance of  2.5515645242854266e-05 which is very low, hence can be dropped
86 Column has variance of  0.0013936678611708101 which is very low, hence can be dropped
87 Column has variance of  0.00016506704640163736 which is very low, hence can be dropped
89 Column has variance of  0.0027429169334404328 which is very low, hence can be dropped
91 Column has variance of  0.007688300488181476 which is very low, hence can be dropped
92 Column has variance of  1.043951756371396e-05 which is very low, hence can be dropped
93 Column has variance of  9.059099482771316e-06 which is very low, hence can be dropped
94 Column has variance of  3.0322927446245835e-08 which is very low, hence can be dropped
95 Column has variance of  1.0901024983984442e-08 which is very low, hence can be dropped
96 Column has variance of  0.04821456154575472 which is very low, hence can be dropped
98 Column has variance of  0.18242276916571443 which is very low, hence can be dropped
99 Column has variance of  0.003936356967681807 which is very low, hence can be dropped
100 Column has variance of  1.2640633880319945e-07 which is very low, hence can be dropped
101 Column has variance of  4.873602555889598e-08 which is very low, hence can be dropped
102 Column has variance of  0.0039649267096042995 which is very low, hence can be dropped
103 Column has variance of  9.391459818765717e-06 which is very low, hence can be dropped
104 Column has variance of  7.237051878120312e-07 which is very low, hence can be dropped
105 Column has variance of  1.0254912194681282e-05 which is very low, hence can be dropped
106 Column has variance of  8.927622595640542e-06 which is very low, hence can be dropped
107 Column has variance of  0.007651836337760147 which is very low, hence can be dropped
108 Column has variance of  0.007526909831427886 which is very low, hence can be dropped
112 Column has variance of  0.0023950727386587455 which is very low, hence can be dropped
113 Column has variance of  0.0001472065857839001 which is very low, hence can be dropped
114 Column has variance of  2.782538181735315e-06 which is very low, hence can be dropped
116 Column has variance of  9.018359277923213e-05 which is very low, hence can be dropped
118 Column has variance of  6.563458496721313e-05 which is very low, hence can be dropped
119 Column has variance of  8.00767608750401e-05 which is very low, hence can be dropped
120 Column has variance of  0.015451581590433616 which is very low, hence can be dropped
121 Column has variance of  0.009923690888719047 which is very low, hence can be dropped
122 Column has variance of  0.8174338282026655 which is very low, hence can be dropped
123 Column has variance of  0.01173212569348089 which is very low, hence can be dropped
124 Column has variance of  0.013028782392326488 which is very low, hence can be dropped
125 Column has variance of  0.07871093279650547 which is very low, hence can be dropped
126 Column has variance of  0.0642474541777042 which is very low, hence can be dropped
127 Column has variance of  0.018335560840219728 which is very low, hence can be dropped
128 Column has variance of  0.06978818897306724 which is very low, hence can be dropped
129 Column has variance of  1.4895683782156268 which is very low, hence can be dropped
130 Column has variance of  0.00681140948330165 which is very low, hence can be dropped
131 Column has variance of  5.065094108927022e-06 which is very low, hence can be dropped
132 Column has variance of  0.002828197864427096 which is very low, hence can be dropped
134 Column has variance of  8.942947204486611 which is very low, hence can be dropped
143 Column has variance of  1.6430351479054807e-06 which is very low, hence can be dropped
144 Column has variance of  0.0037629373318026193 which is very low, hence can be dropped
145 Column has variance of  0.0007044025657975374 which is very low, hence can be dropped
146 Column has variance of  0.00047718164481994963 which is very low, hence can be dropped
147 Column has variance of  0.0007356522448624469 which is very low, hence can be dropped
153 Column has variance of  8.734167889804659e-05 which is very low, hence can be dropped
155 Column has variance of  1.2598418658186799 which is very low, hence can be dropped
156 Column has variance of  0.006268599505444745 which is very low, hence can be dropped
163 Column has variance of  0.014881313662845466 which is very low, hence can be dropped
164 Column has variance of  0.05882256318687977 which is very low, hence can be dropped
165 Column has variance of  0.16591717837444642 which is very low, hence can be dropped
166 Column has variance of  1.2538532884469245 which is very low, hence can be dropped
167 Column has variance of  0.4003939681164857 which is very low, hence can be dropped
168 Column has variance of  0.0022694389923437106 which is very low, hence can be dropped
169 Column has variance of  0.0391713822834871 which is very low, hence can be dropped
170 Column has variance of  0.024796161999506305 which is very low, hence can be dropped
171 Column has variance of  0.0036948002448026944 which is very low, hence can be dropped
172 Column has variance of  0.0050755658751994205 which is very low, hence can be dropped
173 Column has variance of  0.009164992446403014 which is very low, hence can be dropped
174 Column has variance of  0.005076118763304898 which is very low, hence can be dropped
175 Column has variance of  0.013530839840985957 which is very low, hence can be dropped
176 Column has variance of  0.005612675480183944 which is very low, hence can be dropped
177 Column has variance of  0.08003408544510127 which is very low, hence can be dropped
181 Column has variance of  0.050356146480114584 which is very low, hence can be dropped
184 Column has variance of  0.01214353542877193 which is very low, hence can be dropped
195 Column has variance of  0.15617273678118296 which is very low, hence can be dropped
198 Column has variance of  0.28912687634816603 which is very low, hence can be dropped
206 Column has variance of  0.002562459961563105 which is very low, hence can be dropped
209 Column has variance of  1.3643962203715427 which is very low, hence can be dropped
210 Column has variance of  0.0017694254949636586 which is very low, hence can be dropped
211 Column has variance of  0.0006252572111363548 which is very low, hence can be dropped
212 Column has variance of  0.0009971475888599435 which is very low, hence can be dropped
213 Column has variance of  0.002812134652760093 which is very low, hence can be dropped
214 Column has variance of  0.003187242900736602 which is very low, hence can be dropped
215 Column has variance of  0.0009264001095193327 which is very low, hence can be dropped
216 Column has variance of  0.0006637996380541214 which is very low, hence can be dropped
217 Column has variance of  0.0021421589156585996 which is very low, hence can be dropped
218 Column has variance of  1.3699206375617323 which is very low, hence can be dropped
219 Column has variance of  2.709666910824481e-06 which is very low, hence can be dropped
221 Column has variance of  0.000543133593480152 which is very low, hence can be dropped
222 Column has variance of  0.0031289219135326963 which is very low, hence can be dropped
224 Column has variance of  0.005071041370359711 which is very low, hence can be dropped
227 Column has variance of  0.00011568535685511909 which is very low, hence can be dropped
228 Column has variance of  0.00011545240637997116 which is very low, hence can be dropped
238 Column has variance of  2.8830265641469716e-06 which is very low, hence can be dropped
239 Column has variance of  2.0776490443934183e-06 which is very low, hence can be dropped
247 Column has variance of  0.004472960982150241 which is very low, hence can be dropped
248 Column has variance of  0.0024240922937240924 which is very low, hence can be dropped
249 Column has variance of  0.0002487177487222367 which is very low, hence can be dropped
251 Column has variance of  0.00140418766188983 which is very low, hence can be dropped
253 Column has variance of  0.0005028853663715393 which is very low, hence can be dropped
254 Column has variance of  8.339595473694748e-05 which is very low, hence can be dropped
255 Column has variance of  0.014480312089365502 which is very low, hence can be dropped
267 Column has variance of  0.0008790437182667847 which is very low, hence can be dropped
269 Column has variance of  1.3278623084429109 which is very low, hence can be dropped
277 Column has variance of  1.431462163837263 which is very low, hence can be dropped
278 Column has variance of  1.1562307950429757e-07 which is very low, hence can be dropped
279 Column has variance of  0.0004116502037456183 which is very low, hence can be dropped
280 Column has variance of  4.202943019046751e-05 which is very low, hence can be dropped
281 Column has variance of  3.075236236241965e-05 which is very low, hence can be dropped
282 Column has variance of  7.310877011513063e-05 which is very low, hence can be dropped
285 Column has variance of  0.9272202024925948 which is very low, hence can be dropped
288 Column has variance of  8.620655585609012e-06 which is very low, hence can be dropped
289 Column has variance of  2.614667248896111 which is very low, hence can be dropped
290 Column has variance of  0.07343396952895369 which is very low, hence can be dropped
291 Column has variance of  0.0006527422682057519 which is very low, hence can be dropped
298 Column has variance of  0.004124833624482154 which is very low, hence can be dropped
299 Column has variance of  0.01711528069619148 which is very low, hence can be dropped
300 Column has variance of  0.04802548035925769 which is very low, hence can be dropped
301 Column has variance of  0.11021184043908873 which is very low, hence can be dropped
302 Column has variance of  0.039011778068873924 which is very low, hence can be dropped
303 Column has variance of  0.00021055787276010558 which is very low, hence can be dropped
304 Column has variance of  0.004207780405799822 which is very low, hence can be dropped
305 Column has variance of  0.003293323864733424 which is very low, hence can be dropped
306 Column has variance of  0.0006451578550753018 which is very low, hence can be dropped
307 Column has variance of  0.0007544715993618364 which is very low, hence can be dropped
308 Column has variance of  0.0011285041357480682 which is very low, hence can be dropped
309 Column has variance of  0.0007546066849179246 which is very low, hence can be dropped
310 Column has variance of  0.0018887582044687665 which is very low, hence can be dropped
311 Column has variance of  0.0008292196066574446 which is very low, hence can be dropped
312 Column has variance of  0.013762984826786482 which is very low, hence can be dropped
316 Column has variance of  1.0376054107044184 which is very low, hence can be dropped
317 Column has variance of  0.005240653758583965 which is very low, hence can be dropped
318 Column has variance of  1.4784856824773227 which is very low, hence can be dropped
319 Column has variance of  4.748297235151143 which is very low, hence can be dropped
320 Column has variance of  0.0010166399821975734 which is very low, hence can be dropped
321 Column has variance of  4.481665675012632 which is very low, hence can be dropped
323 Column has variance of  6.344652353984891 which is very low, hence can be dropped
331 Column has variance of  0.004022667883023151 which is very low, hence can be dropped
334 Column has variance of  0.029888376975788052 which is very low, hence can be dropped
337 Column has variance of  2.888078659500959 which is very low, hence can be dropped
342 Column has variance of  0.00012811520819987126 which is very low, hence can be dropped
347 Column has variance of  0.12403515444586867 which is very low, hence can be dropped
348 Column has variance of  0.00014071846291313514 which is very low, hence can be dropped
349 Column has variance of  0.00011242800894042184 which is very low, hence can be dropped
350 Column has variance of  0.00020522439258338344 which is very low, hence can be dropped
351 Column has variance of  0.0006033369437810851 which is very low, hence can be dropped
352 Column has variance of  0.00017311069178995876 which is very low, hence can be dropped
353 Column has variance of  0.0002402274658072566 which is very low, hence can be dropped
354 Column has variance of  0.0001707699738411117 which is very low, hence can be dropped
355 Column has variance of  0.0004975884980494316 which is very low, hence can be dropped
356 Column has variance of  0.14970523332802896 which is very low, hence can be dropped
357 Column has variance of  2.509253001278694e-07 which is very low, hence can be dropped
359 Column has variance of  5.0927598464824974e-05 which is very low, hence can be dropped
360 Column has variance of  0.0004001027561593251 which is very low, hence can be dropped
362 Column has variance of  0.00046846512707141334 which is very low, hence can be dropped
365 Column has variance of  7.054390421872338e-06 which is very low, hence can be dropped
366 Column has variance of  5.676063644460448e-06 which is very low, hence can be dropped
367 Column has variance of  7.285035923717533e-06 which is very low, hence can be dropped
368 Column has variance of  4.4381107689022426e-06 which is very low, hence can be dropped
376 Column has variance of  2.855300000817083e-07 which is very low, hence can be dropped
377 Column has variance of  2.1765962592843768e-07 which is very low, hence can be dropped
385 Column has variance of  0.0002063712797978626 which is very low, hence can be dropped
386 Column has variance of  0.00023988228085489242 which is very low, hence can be dropped
387 Column has variance of  2.4893458691841078e-05 which is very low, hence can be dropped
389 Column has variance of  0.00013961795973955034 which is very low, hence can be dropped
391 Column has variance of  4.539670690529095e-05 which is very low, hence can be dropped
392 Column has variance of  8.73529629711132e-06 which is very low, hence can be dropped
393 Column has variance of  0.0014752123041889655 which is very low, hence can be dropped
405 Column has variance of  0.00011509577804474522 which is very low, hence can be dropped
406 Column has variance of  8.006541582696903 which is very low, hence can be dropped
407 Column has variance of  0.13301421099402128 which is very low, hence can be dropped
408 Column has variance of  6.646690706388391 which is very low, hence can be dropped
409 Column has variance of  3.157171928805614 which is very low, hence can be dropped
410 Column has variance of  4.50703532970221 which is very low, hence can be dropped
411 Column has variance of  0.30412375911765377 which is very low, hence can be dropped
416 Column has variance of  1.0721218245803485 which is very low, hence can be dropped
420 Column has variance of  9.34948205353655 which is very low, hence can be dropped
426 Column has variance of  0.991259320665041 which is very low, hence can be dropped
427 Column has variance of  9.254643130984583 which is very low, hence can be dropped
437 Column has variance of  2.5962028463559714 which is very low, hence can be dropped
441 Column has variance of  0.033934322921996614 which is very low, hence can be dropped
442 Column has variance of  0.43453746523763026 which is very low, hence can be dropped
443 Column has variance of  0.020607190357725545 which is very low, hence can be dropped
444 Column has variance of  0.024187001465470325 which is very low, hence can be dropped
445 Column has variance of  0.019952177712835424 which is very low, hence can be dropped
446 Column has variance of  0.031031714405134657 which is very low, hence can be dropped
447 Column has variance of  0.007475463626818293 which is very low, hence can be dropped
448 Column has variance of  0.05582603044177594 which is very low, hence can be dropped
452 Column has variance of  0.8449219817890223 which is very low, hence can be dropped
453 Column has variance of  5.066120575531568 which is very low, hence can be dropped
454 Column has variance of  9.361521769484813 which is very low, hence can be dropped
455 Column has variance of  0.8805422376655848 which is very low, hence can be dropped
459 Column has variance of  1.8105755198289895 which is very low, hence can be dropped
469 Column has variance of  9.933116345587658 which is very low, hence can be dropped
475 Column has variance of  6.8182295359303735 which is very low, hence can be dropped
479 Column has variance of  6.962253864646226 which is very low, hence can be dropped
491 Column has variance of  1.4988684958663911 which is very low, hence can be dropped
493 Column has variance of  0.9485752718627868 which is very low, hence can be dropped
526 Column has variance of  0.9185833359574673 which is very low, hence can be dropped
527 Column has variance of  3.5671786090389914 which is very low, hence can be dropped
539 Column has variance of  1.5697910818881597 which is very low, hence can be dropped
540 Column has variance of  0.5357190761421328 which is very low, hence can be dropped
541 Column has variance of  8.388992772650454 which is very low, hence can be dropped
542 Column has variance of  7.489852087299687e-06 which is very low, hence can be dropped
543 Column has variance of  2.3517591658972197e-06 which is very low, hence can be dropped
544 Column has variance of  8.733216214670339e-08 which is very low, hence can be dropped
545 Column has variance of  1.7306566992676808 which is very low, hence can be dropped
546 Column has variance of  0.15137220405833376 which is very low, hence can be dropped
549 Column has variance of  0.4533944972533438 which is very low, hence can be dropped
551 Column has variance of  1.8526403049429587 which is very low, hence can be dropped
552 Column has variance of  0.07630336949571859 which is very low, hence can be dropped
553 Column has variance of  4.807702119436431 which is very low, hence can be dropped
554 Column has variance of  0.35862371543244337 which is very low, hence can be dropped
556 Column has variance of  1.6384215573532654 which is very low, hence can be dropped
557 Column has variance of  3.4985212478286876 which is very low, hence can be dropped
558 Column has variance of  0.007032421964754232 which is very low, hence can be dropped
559 Column has variance of  0.04055868524944201 which is very low, hence can be dropped
560 Column has variance of  0.0026602720685329926 which is very low, hence can be dropped
563 Column has variance of  0.014824952454537492 which is very low, hence can be dropped
564 Column has variance of  6.935760827293852 which is very low, hence can be dropped
565 Column has variance of  0.006580836421564915 which is very low, hence can be dropped
566 Column has variance of  1.0665956113661326 which is very low, hence can be dropped
567 Column has variance of  0.0010732798612669973 which is very low, hence can be dropped
568 Column has variance of  0.993299610084573 which is very low, hence can be dropped
571 Column has variance of  0.07568652701397191 which is very low, hence can be dropped
573 Column has variance of  0.06174137791144954 which is very low, hence can be dropped
575 Column has variance of  0.004595635745341555 which is very low, hence can be dropped
578 Column has variance of  0.00013760017655111654 which is very low, hence can be dropped
579 Column has variance of  9.293216209553479e-05 which is very low, hence can be dropped
580 Column has variance of  9.70936772565866e-06 which is very low, hence can be dropped
582 Column has variance of  1.1589429400315796e-05 which is very low, hence can be dropped
583 Column has variance of  0.0002951496782670073 which is very low, hence can be dropped
584 Column has variance of  1.3841710146524136e-05 which is very low, hence can be dropped
586 Column has variance of  0.00015272451356909456 which is very low, hence can be dropped
587 Column has variance of  7.757267970327905e-05 which is very low, hence can be dropped
588 Column has variance of  8.220099041533564e-06 which is very low, hence can be dropped
New shape of the data set after deleting above 272  columns is (1567, 183)

# Now lets check how much null values do we have in our data set
Data.isnull().sum()

0              6
1              7
2             14
3             14
4             14
6             14
12             2
15             3
21             2
22             2
23             2
24             2
28             2
40            24
43             1
45             1
46             1
48             1
50             1
51             1
55             4
59             7
60             6
62             6
63             7
64             7
65             7
66             6
67             6
68             6
70             6
71             6
72           794
73           794
88             0
90            51
115            0
117            0
133            8
135            5
136            6
137            7
138           14
139           14
140           14
142           14
148            2
150            3
151            3
152            3
154            3
159            2
160            2
161            2
162            2
180            1
182            1
183            1
185            1
187            1
188            1
196            7
197            6
199            6
200            7
201            7
202            7
203            6
204            6
205            6
207            6
208            6
223            0
225           51
250            0
252            0
268            8
270            5
271            6
272            7
273           14
274           14
275           14
283            2
286            3
287            3
294            2
295            2
296            2
297            2
324            1
332            7
333            6
335            6
336            7
338            7
339            6
340            6
341            6
343            6
344            6
345          794
346          794
361            0
363           51
388            0
390            0
412           14
413           14
415           14
417            2
418            2
419            2
421            2
423            3
424            3
425            3
428           10
429            0
430            2
431            2
432            2
433            2
434            2
435            2
436            2
438            2
439            2
440            2
456            1
457            1
460            1
467            4
468            7
470            6
471            6
472            7
473            7
474            7
476            6
477            6
478            6
480            6
482           24
483           24
484           24
485           24
486           24
487           24
488           24
489           24
490            1
494            0
495            0
496           51
497           51
499            2
500            2
510            2
511            2
519          715
520            0
521            0
522            0
523            0
524            0
525           24
547          260
548          260
550          260
555          260
561            1
562          273
569          273
570            0
572            0
574            0
576            0
577            0
581          949
585            1
589            1
Pass/Fail      0
dtype: int64

Data.isna().sum().sum()
# so we have total of 7585 null values across 183 columns

7585

# Since we do not have domain expertise on each particular feature, we will fill the null values with the mean value 

#Replace blank valus with Nan values
Data = Data.replace('', np.nan)

#Replace the Null value with the mean value of each column
for columns in Data.columns:
    mean = Data[columns].mean()
    Data[columns] = Data[columns].fillna(mean)

#Lets check null values again
Data.isnull().sum().sum()

0

# now that we have our base data ready, lets perform Data analysis & visualisation
#Lets check the distribution of each feature
Data.hist(color='lightblue', edgecolor = 'black', alpha = 0.7, figsize = (20,20), layout=(37,5))
plt.tight_layout()
plt.show()

Data.skew()
# We can see that except for below mentioned columns rest all columns are highly skewed

0             0.514641
1            -0.043163
2            -0.307136
3             1.719310
4            19.733442
6            -0.107427
12            7.209324
15           17.076921
21            2.376138
22           -2.182923
23            0.356874
24           -0.054125
28           -0.063702
40           -1.882466
43            0.554674
45            0.374818
46            0.337946
48           -0.019920
50            0.389282
51           -0.509794
55            0.368893
59            4.730023
60            0.314994
62            5.307243
63           10.706091
64            1.130865
65            3.333722
66           -0.204512
67           20.824463
68           -2.902413
70            0.758319
71            1.254421
72           -9.774366
73           14.111827
88           -0.272824
90            0.347334
115          -0.271651
117          38.005963
133          -0.277546
135           6.192576
136           0.333026
137           0.384090
138           0.823306
139           2.085160
140          19.735009
142          15.039760
148          38.562024
150           1.296333
151          13.005347
152          39.557423
154          27.732438
159           4.206303
160           3.995751
161           2.231207
162           1.829927
180           0.776498
182           2.144810
183           0.201764
185          30.709184
187          24.516484
188           0.766068
196          17.393147
197          16.253846
199          14.588119
200          13.439685
201           9.746788
202          24.733026
203          17.573474
204          17.634821
205          17.887940
207          18.237948
208           0.162865
223          17.146330
225           1.091511
250           6.751706
252          39.560364
268           0.578674
270           5.175073
271           0.348704
272           0.346599
273           1.002222
274           2.178885
275          19.735004
283          38.647193
286          13.968135
287          39.559150
294           4.429284
295           4.224084
296           2.190904
297           1.791628
324           0.706663
332          22.774616
333          18.659509
335          20.293567
336          19.114061
338          27.894916
339          22.019652
340          18.269609
341          17.895266
343          20.570310
344           0.171990
345          12.530000
346          11.243294
361          16.868775
363           1.000129
388           6.285941
390          39.562779
412           1.816378
413          16.359941
415          16.283884
417           2.495089
418           0.456661
419           0.499839
421          37.696746
423           2.084869
424          12.625177
425          36.258005
428           9.207376
429          20.552274
430           7.394316
431           7.103304
432           3.346368
433           1.364392
434           8.543970
435           8.665764
436           8.698782
438          10.164666
439           6.757087
440           0.117124
456          15.791705
457          27.827277
460           1.867412
467          10.454602
468           1.262658
470           8.624263
471          10.642803
472           0.574882
473           3.435317
474           5.466532
476           5.458772
477          19.787756
478          39.585205
480           0.815464
482           0.469546
483           1.714218
484           1.534042
485           1.524456
486           0.615203
487           1.160917
488           0.356705
489           1.047157
490           0.908301
494          16.161844
495          19.099472
496           3.280515
497           1.083132
499           0.743494
500           0.920019
510           4.109312
511           0.700040
519           5.525019
520          17.415222
521           9.040238
522           5.218409
523          21.906221
524          38.854591
525          14.046365
547          -0.452326
548           0.938833
550          13.098494
555           1.924065
561           0.805957
562           0.116481
569           2.256412
570          -8.658927
572           4.298821
574           4.294132
576           4.195898
577           4.169820
581           4.710143
585          24.705005
589           2.715340
Pass/Fail     3.487359
dtype: float64

for columns in Data.columns:
    if Data[columns].skew() <= 0.5 and Data[columns].skew() >= -0.5:
        print( columns , 'column is close to normal distribution with', Data[columns].skew(), 'skewness')

1 column is close to normal distribution with -0.04316264649515132 skewness
2 column is close to normal distribution with -0.3071360279896156 skewness
6 column is close to normal distribution with -0.10742686096931855 skewness
23 column is close to normal distribution with 0.35687433333104274 skewness
24 column is close to normal distribution with -0.05412523101217642 skewness
28 column is close to normal distribution with -0.06370154039777466 skewness
45 column is close to normal distribution with 0.3748180652355466 skewness
46 column is close to normal distribution with 0.33794574731566174 skewness
48 column is close to normal distribution with -0.019919872092288217 skewness
50 column is close to normal distribution with 0.38928231336840213 skewness
55 column is close to normal distribution with 0.3688928821847343 skewness
60 column is close to normal distribution with 0.3149941060125438 skewness
66 column is close to normal distribution with -0.2045121899230426 skewness
88 column is close to normal distribution with -0.27282399081128983 skewness
90 column is close to normal distribution with 0.34733443776774076 skewness
115 column is close to normal distribution with -0.27165065863283777 skewness
133 column is close to normal distribution with -0.27754598654909324 skewness
136 column is close to normal distribution with 0.3330260984322855 skewness
137 column is close to normal distribution with 0.3840900389944301 skewness
183 column is close to normal distribution with 0.20176425747939875 skewness
208 column is close to normal distribution with 0.16286496147345517 skewness
271 column is close to normal distribution with 0.3487037100911712 skewness
272 column is close to normal distribution with 0.34659948108345867 skewness
344 column is close to normal distribution with 0.17198993560941542 skewness
418 column is close to normal distribution with 0.4566608966307543 skewness
419 column is close to normal distribution with 0.4998388672079762 skewness
440 column is close to normal distribution with 0.11712380906663562 skewness
482 column is close to normal distribution with 0.4695460556312447 skewness
488 column is close to normal distribution with 0.35670526829994803 skewness
547 column is close to normal distribution with -0.452326402218069 skewness
562 column is close to normal distribution with 0.11648141179240548 skewness

#Lets check for the outliers in thh dataset
for columns in Data.columns:
    plt.figure()
    plt.title(columns)
    sns.boxplot(data = Data[columns], orient="h" , color = 'pink')

Since we have Outliers present in almost all the columns, changing all the values to adjust outliers in all the columns will change the entire data set. ALso, we have features with only negative values, hence taking log transformation will result in undifined values (NAN). Hence in this data set, we will prefer keeping data set untouched w.r.t outliers, (However with agreement with SME, we can still choose to take care of outliers)

#for correlation, closer the value to 1, higher is the corelation betwen two features
Data.corr()

#Visualize the above correlation using heat map
plt.figure(figsize = (20,18))
sns.heatmap(Data.corr(), annot = True)

<matplotlib.axes._subplots.AxesSubplot at 0x1ecc5b543a0>

# Since highly correlated columns can help building the model in same way, we can drop on of the two highly corelated column 
corr_matrix = Data.corr()
iters = range(len(corr_matrix.columns) - 1)
drop_cols = []

 # Iterate through the correlation matrix and compare correlations
for i in iters:
    for j in range(i-1):
        if i != 0:
            item = abs(corr_matrix.iloc[i, j])
            col = corr_matrix.columns[i]
            row = corr_matrix.index[j]
            # If correlation exceeds the threshold
            if item >= 0.90:
                # Print the correlated features and the correlation value
                print('Columns', col,'and column', row, 'are highly correlated with',item,'ratio')
                drop_cols.append(col)

print ('\nColumns with high correlations which can be dropped are:\n', np.unique(drop_cols))

Columns 50 and column 46 are highly correlated with 0.9044818259166801 ratio
Columns 70 and column 66 are highly correlated with 0.9044609903388564 ratio
Columns 140 and column 4 are highly correlated with 0.9999751247610721 ratio
Columns 152 and column 148 are highly correlated with 0.9910694582650661 ratio
Columns 154 and column 148 are highly correlated with 0.9396050560127642 ratio
Columns 199 and column 196 are highly correlated with 0.9419809272931734 ratio
Columns 204 and column 67 are highly correlated with 0.9022307064188807 ratio
Columns 207 and column 196 are highly correlated with 0.9167918615124543 ratio
Columns 252 and column 117 are highly correlated with 0.9861933657838743 ratio
Columns 270 and column 135 are highly correlated with 0.9464739349956607 ratio
Columns 271 and column 136 are highly correlated with 0.9712649283173527 ratio
Columns 272 and column 137 are highly correlated with 0.9768378109004264 ratio
Columns 273 and column 138 are highly correlated with 0.9191717004943438 ratio
Columns 274 and column 139 are highly correlated with 0.985728144360803 ratio
Columns 275 and column 4 are highly correlated with 0.9999755698304483 ratio
Columns 275 and column 140 are highly correlated with 0.9999999763805127 ratio
Columns 283 and column 148 are highly correlated with 0.9995218286822887 ratio
Columns 283 and column 152 are highly correlated with 0.9917748644859977 ratio
Columns 283 and column 154 are highly correlated with 0.9362410275128739 ratio
Columns 286 and column 151 are highly correlated with 0.9900031988390751 ratio
Columns 287 and column 148 are highly correlated with 0.9910771233820856 ratio
Columns 287 and column 152 are highly correlated with 0.9999970976317996 ratio
Columns 287 and column 283 are highly correlated with 0.9917862663223181 ratio
Columns 294 and column 159 are highly correlated with 0.9932395425046503 ratio
Columns 295 and column 160 are highly correlated with 0.9964804116578476 ratio
Columns 296 and column 161 are highly correlated with 0.9946485928894574 ratio
Columns 297 and column 162 are highly correlated with 0.9884014877258684 ratio
Columns 324 and column 188 are highly correlated with 0.97530226992646 ratio
Columns 332 and column 196 are highly correlated with 0.9638588005990898 ratio
Columns 332 and column 197 are highly correlated with 0.9050712800485732 ratio
Columns 332 and column 199 are highly correlated with 0.90309501091955 ratio
Columns 332 and column 207 are highly correlated with 0.9145387691147532 ratio
Columns 333 and column 197 are highly correlated with 0.9825963154934192 ratio
Columns 335 and column 196 are highly correlated with 0.9310646142679881 ratio
Columns 335 and column 199 are highly correlated with 0.963222869738428 ratio
Columns 335 and column 332 are highly correlated with 0.9614071701136431 ratio
Columns 336 and column 196 are highly correlated with 0.9114108834493966 ratio
Columns 336 and column 207 are highly correlated with 0.9014946530204482 ratio
Columns 336 and column 332 are highly correlated with 0.9416465043771408 ratio
Columns 336 and column 333 are highly correlated with 0.9019285474205442 ratio
Columns 338 and column 202 are highly correlated with 0.9910117969269783 ratio
Columns 339 and column 203 are highly correlated with 0.9827091137191302 ratio
Columns 340 and column 67 are highly correlated with 0.9466368752862829 ratio
Columns 340 and column 204 are highly correlated with 0.9853734316133732 ratio
Columns 341 and column 67 are highly correlated with 0.9053174973585882 ratio
Columns 341 and column 205 are highly correlated with 0.991697006650239 ratio
Columns 343 and column 196 are highly correlated with 0.9009438618357838 ratio
Columns 343 and column 207 are highly correlated with 0.9847308409275753 ratio
Columns 343 and column 332 are highly correlated with 0.9355397199453718 ratio
Columns 343 and column 335 are highly correlated with 0.9182601200118453 ratio
Columns 343 and column 336 are highly correlated with 0.9196307731648184 ratio
Columns 344 and column 208 are highly correlated with 0.9636875346683662 ratio
Columns 361 and column 223 are highly correlated with 0.9788025881044046 ratio
Columns 363 and column 225 are highly correlated with 0.9634701123157058 ratio
Columns 388 and column 250 are highly correlated with 0.9746150086226977 ratio
Columns 390 and column 117 are highly correlated with 0.9862339054518461 ratio
Columns 390 and column 252 are highly correlated with 0.9999914227740386 ratio
Columns 413 and column 4 are highly correlated with 0.9384157839693659 ratio
Columns 413 and column 140 are highly correlated with 0.9380778546861439 ratio
Columns 413 and column 275 are highly correlated with 0.938091794288112 ratio
Columns 415 and column 142 are highly correlated with 0.9926143870057542 ratio
Columns 421 and column 148 are highly correlated with 0.9988153783986575 ratio
Columns 421 and column 152 are highly correlated with 0.9835906743060074 ratio
Columns 421 and column 154 are highly correlated with 0.9543904536236193 ratio
Columns 421 and column 283 are highly correlated with 0.9978742631844328 ratio
Columns 421 and column 287 are highly correlated with 0.9835961853911365 ratio
Columns 424 and column 151 are highly correlated with 0.9762310553985684 ratio
Columns 424 and column 286 are highly correlated with 0.9669621166080141 ratio
Columns 425 and column 148 are highly correlated with 0.9618511427358277 ratio
Columns 425 and column 152 are highly correlated with 0.9750918692373749 ratio
Columns 425 and column 283 are highly correlated with 0.962216547566731 ratio
Columns 425 and column 287 are highly correlated with 0.9749103402648396 ratio
Columns 425 and column 421 are highly correlated with 0.95460076984744 ratio
Columns 434 and column 430 are highly correlated with 0.948040387326675 ratio
Columns 434 and column 431 are highly correlated with 0.933359481656668 ratio
Columns 435 and column 430 are highly correlated with 0.9516726155512237 ratio
Columns 435 and column 431 are highly correlated with 0.9295443808853142 ratio
Columns 436 and column 430 are highly correlated with 0.9537232380401666 ratio
Columns 436 and column 431 are highly correlated with 0.9325979460533955 ratio
Columns 436 and column 434 are highly correlated with 0.9923330260757832 ratio
Columns 457 and column 185 are highly correlated with 0.9967773319711016 ratio
Columns 471 and column 199 are highly correlated with 0.9382814829148212 ratio
Columns 477 and column 67 are highly correlated with 0.9218150346605667 ratio
Columns 477 and column 205 are highly correlated with 0.9866216754851614 ratio
Columns 477 and column 332 are highly correlated with 0.9062085846699987 ratio
Columns 477 and column 341 are highly correlated with 0.9882192885279739 ratio
Columns 495 and column 223 are highly correlated with 0.9966769257758836 ratio
Columns 495 and column 361 are highly correlated with 0.9748289745381955 ratio
Columns 497 and column 225 are highly correlated with 0.9930712184247211 ratio
Columns 497 and column 363 are highly correlated with 0.9611210453127839 ratio
Columns 522 and column 250 are highly correlated with 0.985970805836949 ratio
Columns 522 and column 388 are highly correlated with 0.9640282170040964 ratio
Columns 524 and column 117 are highly correlated with 0.9786433214466849 ratio
Columns 524 and column 252 are highly correlated with 0.9958043409065614 ratio
Columns 524 and column 390 are highly correlated with 0.9956676752306468 ratio
Columns 576 and column 572 are highly correlated with 0.994772146241892 ratio

Columns with high correlations which can be dropped are:
 ['140' '152' '154' '199' '204' '207' '252' '270' '271' '272' '273' '274'
 '275' '283' '286' '287' '294' '295' '296' '297' '324' '332' '333' '335'
 '336' '338' '339' '340' '341' '343' '344' '361' '363' '388' '390' '413'
 '415' '421' '424' '425' '434' '435' '436' '457' '471' '477' '495' '497'
 '50' '522' '524' '576' '70']

print ('As per above correlation calculation, we can further drop', len(np.unique(drop_cols)), 'number of features')
drop_cols_temp = np.unique(drop_cols)
drop_cols_temp

As per above correlation calculation, we can further drop 53 number of features

array(['140', '152', '154', '199', '204', '207', '252', '270', '271',
       '272', '273', '274', '275', '283', '286', '287', '294', '295',
       '296', '297', '324', '332', '333', '335', '336', '338', '339',
       '340', '341', '343', '344', '361', '363', '388', '390', '413',
       '415', '421', '424', '425', '434', '435', '436', '457', '471',
       '477', '495', '497', '50', '522', '524', '576', '70'], dtype='<U3')

print ('shape of the Data set before dropping highly correlated features is', Data.shape)
for i in drop_cols_temp:
    del Data[i]

print ('shape of the Data set after dropping highly correlated features is', Data.shape)

shape of the Data set before dropping highly correlated features is (1567, 183)
shape of the Data set after dropping highly correlated features is (1567, 130)

new_corr_matrix = Data.corr()
new_corr_matrix.tail(1)
#From below, we can see that out of all the columns, column number  21,59, 430,431, 510 has a little better correlation with Pass/Fal column

#Lets see bivariate analysis for above column with respect to Pass/Fail column
sns.lmplot(x='21',y='59',data = Data, hue = 'Pass/Fail')
# we can see the distribution of the pass and fail conductors is situated on extreme negative values of feature 21

<seaborn.axisgrid.FacetGrid at 0x1ecc2662fa0>

sns.lmplot(x='430',y='431',data = Data, hue = 'Pass/Fail')
#We can see a positive slope between 430 and 431 and distribution is mostly concentrated new 0

<seaborn.axisgrid.FacetGrid at 0x1ecd8c75670>

sns.lmplot(x='510',y='431',data = Data, hue = 'Pass/Fail')
# we can see that there are many outliers for feaure 510

<seaborn.axisgrid.FacetGrid at 0x1ecc6cb37c0>

#Lets check the value counts for each Pass/Fail condutors
Data['Pass/Fail'].value_counts()
# we can see that data is highly imbalance, so we can try balancing it and then creating the models on same

-1    1463
 1     104
Name: Pass/Fail, dtype: int64

# now that we have cleaned the data and visualized it, lets devide the data into training and testing samples
x = Data.drop(columns = ['Pass/Fail'])
y = Data['Pass/Fail']
x.shape , y.shape

((1567, 129), (1567,))

#Split into taining and testing data
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.30, random_state=1)
X_train.shape, X_test.shape, y_train.shape, y_test.shape

((1096, 129), (471, 129), (1096,), (471,))

from imblearn.over_sampling import SMOTE

print("Before Upsampling, counts of label '-1': {}".format(sum(y_train==-1)))
print("Before Upsampling, counts of label '1': {} \n".format(sum(y_train==1)))

sm = SMOTE(k_neighbors = 5, random_state=1)   #Synthetic Minority Over Sampling Technique
X_train_res, y_train_res = sm.fit_sample(X_train, y_train.ravel())


print("After Upsampling, counts of label '-1': {}".format(sum(y_train_res==-1)))
print("After Upsampling, counts of label '1': {} \n".format(sum(y_train_res==1)))



print('After Upsampling, the shape of X_train_X: {}'.format(X_train_res.shape))
print('After Upsampling, the shape of Y_train: {} \n'.format(y_train_res.shape))

Before Upsampling, counts of label '-1': 1024
Before Upsampling, counts of label '1': 72 

After Upsampling, counts of label '-1': 1024
After Upsampling, counts of label '1': 1024 

After Upsampling, the shape of X_train_X: (2048, 129)
After Upsampling, the shape of Y_train: (2048,)

# Lets build the lgistic model for above
from sklearn.linear_model import LogisticRegression

LR_model = LogisticRegression()
LR_model.fit(X_train_res,y_train_res)

# Prediction on of test data
y_pred = LR_model.predict(X_test)
LR_model_score = LR_model.score(X_test, y_test)

# Let's measure the accuracy of this model's prediction
print('Accuracy of the Logistic regression model on Original data is', LR_model_score*100)
#We can see that models performance is increased due to scaled values
print('\n\n Classification Report:')
print(metrics.classification_report(y_test, y_pred))

Accuracy of the Logistic regression model on Original data is 69.00212314225053


 Classification Report:
              precision    recall  f1-score   support

          -1       0.93      0.72      0.81       439
           1       0.07      0.28      0.11        32

    accuracy                           0.69       471
   macro avg       0.50      0.50      0.46       471
weighted avg       0.87      0.69      0.76       471

# Plotting confusion matrix
plot_confusion_matrix(LR_model,X_test,y_test)

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1ecd95b6130>

Explanation of Classification report:¶

Recall:
Model predicted 316 cases correctly as semicondutor are passed while predicted 123 to be fail where as they were actually pass(this is Recall)
Model predicted 23 cases incorrectly as semicondutor are pass where as they were fail and predicted 9 to be fail which are actually fail (this is Recall)

Precision:
We can see that our model's precision in predicting Pass(-1) is 93% as it has rightly predicted 316 condutors as Pass out of (316+23) total number of pass conductors and model's precision in predicting Fail(1) is 7% as it has incorrectly predicted 123 condutors as Fail out of (123+9) total number of Fail conductors

F1 score is the middle way / average of both Precision and Recall: Hence our model's F1-score for Pass conductors is 81% and for Fail conductors is 11%

And lastly, Model's overall accuracy is 69%

# Lets  implement K fold cross validation
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

kfold = KFold(n_splits=10, random_state=1)
model = LogisticRegression()
KFCV_results = cross_val_score(model, X_train_res, y_train_res, cv=kfold)
print('Accuracy for 10 folds is',KFCV_results)
print(" Overall Accuracy and its standard deviation is: %.3f%% (%.3f%%)" % (KFCV_results.mean()*100.0, KFCV_results.std()*100.0))

Accuracy for 10 folds is [0.70731707 0.67317073 0.60487805 0.66829268 0.72195122 0.71707317
 0.74634146 0.76585366 0.70098039 0.76470588]
 Overall Accuracy and its standard deviation is: 70.706% (4.666%)

#WE can assume that this after applying k fold cross validation our model is likely
# to perform with the accuracy of between 66% to 74 % in production

# Lets implement LeaveoneOut cross validation
from sklearn.model_selection import LeaveOneOut

LOOCV_results = cross_val_score(model, X_train_res, y_train_res, cv=LeaveOneOut())
print('Accuracy for leave one out CV is',LOOCV_results)
print(" Overall Accuracy and its standard deviation is: %.3f%% (%.3f%%)" % (LOOCV_results.mean()*100.0, LOOCV_results.std()*100.0))

Accuracy for leave one out CV is [1. 1. 1. ... 0. 1. 1.]
 Overall Accuracy and its standard deviation is: 76.074% (42.663%)

# Lets implement StratifiedKFold cross validation

from sklearn.model_selection  import StratifiedKFold
stratified_kfold_CV = StratifiedKFold(n_splits = 10, random_state = 1)
StatifiedKF_results = cross_val_score(model, X_train_res, y_train_res, cv = stratified_kfold_CV)
print('Accuracy for stratified kfold CV is',StatifiedKF_results)
print(" Overall Accuracy and its standard deviation is: %.3f%% (%.3f%%)" % (StatifiedKF_results.mean()*100.0, StatifiedKF_results.std()*100.0))

Accuracy for stratified kfold CV is [0.70731707 0.71707317 0.78536585 0.77073171 0.75121951 0.77560976
 0.74634146 0.7902439  0.7745098  0.76960784]
 Overall Accuracy and its standard deviation is: 75.880% (2.662%)

We can see that the best accuracy which is observed is with leave one out CV, however since it is time and resource consuming, we can consider Stratified k fold CV which is gives average accuracy of around 73% to 77%.

#Lets apply Gridsearch CV to find best hyper parameters
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV

LR_GSV = LogisticRegression()

# specify parameters and distributions to sample from
LR_GSV_param = {"penalty" : ['l1', 'l2', 'elasticnet', None],
              "solver" : ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],
              "dual": [True, False]}

# run randomized search
samples = 10  # number of random samples 
randomCV = RandomizedSearchCV(LR_GSV, param_distributions=LR_GSV_param, n_iter=samples) #default cv = 3
randomCV.fit(X_train_res, y_train_res)
print('Parameters suggested as per random CV', randomCV.best_params_)

# run Grid search
grid_search = GridSearchCV(LR_GSV, param_grid=LR_GSV_param)
grid_search.fit(X_train_res, y_train_res)
print('Parameters suggested as per Grid search CV', grid_search.best_params_)

Parameters suggested as per random CV {'solver': 'sag', 'penalty': 'l2', 'dual': False}
Parameters suggested as per Grid search CV {'dual': False, 'penalty': 'l2', 'solver': 'newton-cg'}

LR_GSV_1 = LogisticRegression(solver ='newton-cg', penalty = 'l2', dual =  False)
LR_GSV_1.fit(X_train_res,y_train_res)

# Prediction on of test data
y_pred = LR_GSV_1.predict(X_test)
LR_GSV_1_score = LR_GSV_1.score(X_test, y_test)

# Let's measure the accuracy of this model's prediction
print('Accuracy of the Logistic regression model on Original data is', LR_GSV_1_score*100)
#We can see that models performance is increased due to scaled values
print('\n\n Classification Report:')
print(metrics.classification_report(y_test, y_pred))
# Plotting confusion matrix
print ('Confusion matrix')
plot_confusion_matrix(LR_GSV_1,X_test,y_test)

Accuracy of the Logistic regression model on Original data is 80.89171974522293


 Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.85      0.89       439
           1       0.12      0.28      0.17        32

    accuracy                           0.81       471
   macro avg       0.53      0.56      0.53       471
weighted avg       0.89      0.81      0.84       471

Confusion matrix

<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1ecda3dc670>

For solver ='lbfgs': Models performance is 69.02% and solver ='newton-cg': Models performance is 80.89%
(Note: I have changed the solver value and noted the accuracy with solver ='lbfgs')

#lets try different techniques to get the best results
# standardise the training data by applying zscore
x_sc = x.apply(zscore)
x_sc.head()

Lets reduce the dimensions using PCA¶

We can reduce dimensions using PCA by following below steps:

Calculate the covariance matrix.
Calculate the eigenvectors and their eigenvalues.
Sort the eigenvectors according to their eigenvalues in descending order.
Choose the first K eigenvectors (where k is the dimension we'd like to end up with).
Build new dataset with reduced dimensionality. (For this particular module lets try implementing PCA manually, in the next modules we can straight away use PCA.fit_transform funtion to reduce dimensions)

# Calculate the covariance matrix
cov_matrix = np.cov(x_sc.T)
print("cov_matrix shape:",cov_matrix.shape)
print("Covariance_matrix",cov_matrix)

cov_matrix shape: (129, 129)
Covariance_matrix [[ 1.00063857e+00 -1.43931658e-01  4.75868246e-03 ... -1.71312713e-02
   2.36043864e-02  4.17663212e-03]
 [-1.43931658e-01  1.00063857e+00  5.77088798e-03 ...  5.40409063e-02
   2.27405073e-03  4.48254521e-02]
 [ 4.75868246e-03  5.77088798e-03  1.00063857e+00 ... -7.05872633e-03
   1.57621501e-02 -3.29109770e-02]
 ...
 [-1.71312713e-02  5.40409063e-02 -7.05872633e-03 ...  1.00063857e+00
  -4.71262486e-04  5.85661312e-01]
 [ 2.36043864e-02  2.27405073e-03  1.57621501e-02 ... -4.71262486e-04
   1.00063857e+00 -3.80249376e-03]
 [ 4.17663212e-03  4.48254521e-02 -3.29109770e-02 ...  5.85661312e-01
  -3.80249376e-03  1.00063857e+00]]

#Calculating Eigen Vectors & Eigen Values:

eigenvalues, eigenvectors = np.linalg.eig(cov_matrix)
print('Eigen Vectors \n%s', eigenvectors)
print('\n Eigen Values \n%s', eigenvalues)

Eigen Vectors 
%s [[-0.01079434  0.03400482  0.0236541  ...  0.02264396  0.13738896
   0.03971213]
 [ 0.00086035 -0.01157146 -0.04057078 ... -0.19063241  0.19675594
  -0.08778818]
 [-0.007367   -0.00467633 -0.02219941 ...  0.00185485  0.01244536
  -0.00107696]
 ...
 [-0.00658125  0.02272129 -0.05324648 ...  0.01743134  0.08986486
   0.03622402]
 [ 0.00679145  0.00763494 -0.02008637 ... -0.13034193  0.05354158
  -0.05498246]
 [ 0.02523848  0.00175913 -0.04945522 ...  0.02804211  0.05842212
   0.05312988]]

 Eigen Values 
%s [9.49893989e+00 5.23201935e+00 4.64834800e+00 4.18673132e+00
 3.56295506e+00 3.37810472e+00 3.12345870e+00 2.89674025e+00
 2.72000651e+00 2.51900881e+00 2.28411495e+00 2.18189837e+00
 2.02664007e+00 2.00026471e+00 1.91469766e+00 1.83645407e+00
 1.79402083e+00 1.76711618e+00 1.71298676e+00 1.63450091e+00
 1.62291304e+00 1.53812380e+00 1.49349202e+00 1.47674953e+00
 1.43242788e+00 1.41945383e+00 1.37423738e+00 1.35481822e+00
 1.29826108e+00 1.25538723e+00 1.25190102e+00 1.19926256e+00
 1.19093401e+00 1.17007265e+00 1.15445231e+00 1.14611178e+00
 7.03481832e-02 5.94155090e-02 5.19960118e-02 8.08151147e-02
 3.31104019e-02 2.61503845e-02 1.13672967e+00 3.85521310e-02
 3.00886270e-02 1.84406380e-02 9.52933592e-02 1.40946935e-02
 1.10152905e-02 1.36160982e-03 1.55641540e-03 2.33481595e-03
 6.84030772e-03 3.51769377e-03 1.15027072e-01 1.17828022e-01
 1.26178577e-01 4.88641982e-03 4.17592697e-03 1.44688904e-01
 1.63469669e-01 1.10585649e+00 1.10055425e+00 1.55510403e-01
 1.90579638e-01 1.08439546e+00 1.07150209e+00 1.98992777e-01
 2.04952777e-01 2.17683163e-01 2.38167112e-01 1.04974106e+00
 2.54600983e-01 2.67590125e-01 3.32886077e-01 3.53986344e-01
 1.03718238e+00 3.10791061e-01 3.11860247e-01 1.01219777e+00
 3.73243205e-01 3.97982247e-01 3.02732135e-01 4.12843785e-01
 4.31699740e-01 4.43430161e-01 5.31305846e-01 4.52064251e-01
 2.85354533e-01 5.05880867e-01 5.18539264e-01 5.60532457e-01
 5.77507549e-01 4.88154781e-01 1.00452245e+00 6.19578310e-01
 4.80912499e-01 4.70548386e-01 9.95808246e-01 6.38414545e-01
 6.31251028e-01 9.76647103e-01 6.69512273e-01 6.75743546e-01
 2.82148562e-01 9.63869054e-01 7.02580147e-01 7.14764394e-01
 9.53139311e-01 9.44533513e-01 9.56803478e-01 7.30332879e-01
 9.27678718e-01 7.46779805e-01 7.76660204e-01 7.80506671e-01
 8.84814936e-01 8.94329204e-01 9.05194588e-01 8.45432166e-01
 9.04127955e-01 8.33806830e-01 8.60588941e-01 8.17227089e-01
 7.66834865e-01 8.21746586e-01 8.65501139e-01 8.02081294e-01
 8.02132841e-01]

#Sort eigenvalues in descending order

# Make a set of (eigenvalue, eigenvector) pairs:
eig_pairs = [(eigenvalues[i], eigenvectors[:,i]) for i in range(len(eigenvalues))]

# Sort the (eigenvalue, eigenvector) pairs from highest to lowest with respect to eigenvalue
eig_pairs.sort()

eig_pairs.reverse()
print(eig_pairs)

# Extract the descending ordered eigenvalues and eigenvectors
eigvalues_sorted = [eig_pairs[index][0] for index in range(len(eigenvalues))]
eigvectors_sorted = [eig_pairs[index][1] for index in range(len(eigenvalues))]

# Let's confirm our sorting worked, print out eigenvalues
print('Eigenvalues in descending order: \n%s' %eigvalues_sorted)

[(9.498939889577516, array([-1.07943385e-02,  8.60347823e-04, -7.36699964e-03, -8.67263362e-05,
        2.32333249e-03,  6.09987282e-03, -7.29962812e-03,  2.65589280e-02,
       -1.04009577e-02,  9.74364760e-03, -1.13800972e-02,  3.18999152e-03,
       -4.67133877e-04, -5.12040213e-03,  1.95403670e-02,  4.78906416e-02,
        5.37694841e-02, -1.37714779e-02,  2.09272951e-02,  1.15956203e-02,
        1.29666014e-01, -1.88266088e-02,  1.82670423e-01,  1.71921261e-01,
        4.63501681e-02,  1.11416946e-01,  3.19245835e-02,  2.67406421e-01,
       -1.35291133e-01, -2.06200355e-02, -7.63062129e-02,  1.42007304e-01,
       -4.90055702e-03, -6.78387430e-03,  1.93576464e-03, -2.79738631e-03,
       -2.32613986e-02,  5.13119293e-03, -2.05609629e-02, -2.79152016e-02,
       -1.53163061e-02,  9.14320625e-05,  3.18520868e-03,  1.64434698e-03,
       -4.69129433e-03,  6.66116574e-02, -1.21115536e-02, -9.96200336e-03,
       -4.73455990e-03, -9.65299957e-03,  8.04504124e-03,  5.77981174e-02,
        2.15502051e-02,  6.53289721e-03,  1.23016733e-02,  1.27811386e-02,
        3.02870644e-01,  2.82157033e-01,  2.21847179e-01,  1.87114386e-01,
        2.49544663e-01,  2.73945556e-01,  2.80691928e-01,  3.25371193e-02,
       -5.91253281e-03, -3.65616414e-03, -3.94444145e-03,  2.33162937e-02,
        2.25848113e-01,  2.19446040e-01, -5.55710566e-04,  1.12221522e-02,
        2.01210180e-03,  5.15064937e-03, -1.65706296e-02, -7.18886889e-03,
        2.76667889e-03, -1.17359952e-02, -1.11301897e-02, -4.83874557e-03,
       -4.44411182e-03,  1.02492935e-03, -1.31203589e-03,  2.21078435e-03,
        6.22158593e-03, -1.08572214e-02, -1.58746919e-02,  1.45436102e-02,
        2.42302105e-01,  6.19574362e-02,  1.36711042e-01,  2.23079175e-01,
        1.61719093e-01,  1.46770947e-01,  4.64640548e-02,  4.31831365e-03,
       -1.38280028e-02,  1.32301848e-02,  9.41574685e-03,  5.56070386e-03,
       -6.68976684e-03, -1.05485361e-02,  3.79870937e-03, -3.23701685e-03,
       -7.53302721e-04,  9.40043253e-03, -1.98350672e-03, -1.73956206e-02,
        8.58873857e-03,  2.01289235e-02,  3.71450862e-04,  3.80476390e-03,
        3.90815221e-03,  9.93383696e-02,  4.01607382e-03,  9.87485638e-03,
       -6.43990155e-03,  6.82346856e-03,  1.89083390e-03,  1.25276830e-02,
        4.63236696e-03, -5.32375881e-03, -2.91895174e-02,  1.71232166e-02,
        1.58216709e-02,  2.24714437e-02, -6.58124830e-03,  6.79145008e-03,
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       -5.75897460e-03,  1.89182893e-03, -2.77114501e-03,  1.92487061e-03,
       -8.69585811e-03,  3.14456426e-02,  1.96620409e-01, -1.93979044e-01,
        1.10400592e-02, -2.15805106e-01, -1.77432424e-02,  3.47716976e-03,
       -8.72766775e-04,  4.21415257e-04, -9.60614801e-04,  1.30440286e-03,
       -2.67762023e-03,  2.16113194e-03, -3.63314419e-03, -3.43115150e-04,
        9.81816566e-04, -1.57779605e-03, -2.81366852e-03, -6.54964469e-04,
       -6.48096172e-04, -2.65223108e-03,  2.20688343e-03,  8.64791534e-04,
       -3.12812163e-03,  1.30846298e-03, -1.77286191e-03, -1.97823743e-03,
       -4.17615130e-03,  2.78613517e-03, -6.66260106e-04, -1.56434862e-04,
        6.12998707e-05, -6.92128160e-04, -6.54940281e-03,  1.17827906e-01,
       -1.18154398e-01, -4.16763844e-03, -1.86216739e-03,  1.61751697e-03,
        4.50836342e-03])), (0.0035176937721913258, array([ 2.26033838e-03, -2.14830761e-04, -1.43940877e-03, -3.95659901e-03,
       -1.51796463e-03,  1.03306727e-03,  4.62274856e-04, -1.19970169e-03,
        1.62781010e-03, -5.15089779e-04,  1.79531105e-03, -6.41021020e-04,
       -1.14756446e-03, -2.84372581e-03, -1.82191342e-01,  8.19997193e-03,
       -2.56194093e-02, -1.22542486e-01, -1.34197654e-02,  2.61090500e-04,
       -2.83082544e-04,  1.46658917e-01, -7.34312550e-02,  1.74159046e-01,
       -3.35354551e-01,  2.48038930e-01,  7.15113593e-02,  2.14590856e-02,
        1.08399144e-01,  1.52263833e-03,  8.62124167e-04, -1.48345469e-02,
        1.00900293e-03,  1.59595487e-03, -2.17165988e-03, -5.10545053e-05,
       -5.98673940e-03,  2.36364962e-03, -1.51574065e-04,  2.32168661e-04,
        3.23875469e-03,  1.13236286e-02,  4.64661458e-03,  1.47751256e-04,
       -1.49938343e-03,  1.69045075e-03, -1.17540121e-02,  6.66793478e-03,
        6.25601918e-04,  3.05047681e-03,  6.64088094e-03,  8.98357771e-03,
       -1.46545211e-03, -1.03030130e-02, -1.31560662e-02,  1.83607454e-03,
        9.84396271e-02, -1.24772304e-02,  1.55614096e-02,  1.37887930e-01,
       -3.58557105e-01,  2.32665739e-02, -2.59146370e-02, -5.87781691e-04,
       -3.24597516e-03,  4.50755843e-04,  2.31470472e-03, -8.21099802e-04,
       -4.98273926e-01,  4.86051560e-01, -1.56079708e-02, -1.13542019e-04,
       -5.91571465e-04,  1.69986801e-03, -2.64613505e-04,  3.39223122e-03,
       -1.07584953e-03,  2.15949725e-02, -2.24216894e-02, -1.53765602e-03,
       -2.40404779e-04, -9.71818292e-04, -1.17186822e-03, -5.92719726e-03,
        2.01333478e-03, -5.93733788e-03, -1.25593960e-03, -3.78281635e-03,
        3.99915777e-03,  8.94688490e-03, -1.40574078e-01,  1.43216768e-01,
        8.52476606e-03,  7.53167956e-02, -1.56185502e-02,  1.32164924e-03,
        3.78669256e-03, -4.68188571e-04,  2.23497360e-03,  4.03406938e-03,
        2.89027290e-03, -7.59769200e-04, -8.87422321e-04,  1.30350048e-03,
        1.85439922e-03, -3.50691499e-03, -2.98466847e-04,  3.60022294e-04,
       -1.20533325e-03,  2.54511304e-03, -8.78714701e-04, -6.61736149e-03,
        5.82859280e-03,  2.88617883e-03,  2.61633587e-04,  2.10560893e-03,
        6.86042480e-06, -1.07352969e-04,  8.79955965e-04,  6.09095119e-04,
        2.35722549e-04, -3.70126634e-03, -1.10851690e-03,  7.35562419e-02,
       -6.73327071e-02, -5.03110216e-03, -2.46734898e-03,  5.61366346e-05,
       -2.00304175e-03])), (0.0023348159473509968, array([ 2.29802537e-03, -1.85558754e-03, -5.98703758e-04,  2.83574546e-03,
        3.68992868e-04, -6.62982613e-04,  1.78415543e-04,  2.46473006e-03,
        1.86680855e-04,  2.63742945e-04,  1.53741206e-03, -1.87319877e-03,
       -3.72878889e-03, -1.87885061e-03,  1.13005135e-01, -5.10055821e-01,
       -1.08661881e-01,  7.70603126e-02,  7.38389359e-03,  3.44193256e-03,
       -1.59604271e-03, -1.52401083e-01,  4.83718321e-01, -1.92547945e-01,
        4.11553612e-01,  8.72740386e-02,  1.83022175e-01, -1.18231416e-01,
       -8.70328441e-02, -2.52706560e-03, -1.23370858e-02, -2.42304653e-02,
        5.23398097e-04,  1.77236373e-03, -5.76835799e-04,  1.27715828e-03,
        1.34057757e-03,  3.64588170e-04,  1.02038845e-03, -3.35589818e-03,
        3.10467495e-03, -3.44149211e-03, -2.48859112e-04, -5.03682307e-04,
        1.58329814e-03,  8.96645897e-04,  1.65545919e-02, -1.91041225e-02,
       -2.68128015e-03, -2.07998714e-03,  3.89214581e-03, -1.55808033e-03,
        8.80476942e-03, -2.14929061e-02, -4.08116933e-02, -2.58095205e-03,
       -2.17601741e-01,  3.42729271e-02,  2.21168826e-02,  1.09351695e-01,
       -2.09217893e-01,  6.21844546e-02, -1.74322278e-02,  9.22692084e-03,
        1.70676481e-04,  3.44633713e-03, -4.39992591e-03, -1.68145615e-03,
       -1.40159937e-01,  1.51243438e-01,  2.32486220e-03,  5.56154791e-04,
       -9.86324096e-04,  1.12899932e-03, -3.38970002e-03, -2.18173231e-04,
       -1.37129037e-03, -3.85650125e-02,  3.77408245e-02,  1.10614755e-03,
        1.61633120e-04,  1.08559119e-04,  4.51718283e-04,  3.07271783e-05,
       -2.39381957e-04,  4.11786111e-03,  2.02223481e-03,  1.93378115e-03,
        1.59480751e-02, -2.39825090e-02, -6.15693336e-03,  4.55900994e-02,
        5.44076691e-03,  1.09831791e-01, -8.43058474e-03,  2.04762499e-03,
       -1.76913576e-03,  2.10786170e-03,  1.21825575e-03,  8.95967564e-04,
       -6.02971846e-04, -4.36798642e-04, -4.35023753e-05,  2.48487671e-03,
       -1.39733985e-03,  5.51749227e-03, -1.66623447e-03,  1.28410307e-03,
        1.90990597e-04, -2.13717709e-05,  1.88559349e-03, -1.43268829e-03,
       -2.00999803e-03, -2.54286120e-03,  1.56050256e-05, -4.53698602e-04,
       -1.03136082e-03,  9.71231393e-04,  2.01110665e-03,  1.17524047e-04,
        1.49471739e-03,  2.47637891e-03,  1.22600887e-04,  7.35610934e-02,
       -6.34831027e-02, -6.83899715e-03,  3.29874067e-03, -1.57779204e-03,
       -7.40728396e-04])), (0.0015564153983103311, array([-1.50990251e-03, -1.63544407e-04, -1.02869756e-03, -3.68955505e-03,
       -1.96151837e-03, -1.57369298e-03, -9.77064634e-04, -8.36821130e-04,
        4.60101589e-02,  4.41163899e-02,  7.62562832e-04, -4.59361702e-04,
        2.82251291e-03, -2.59911183e-03, -3.14352693e-02,  5.38839703e-03,
        6.40061890e-02, -1.61260280e-02,  2.07736880e-03,  6.85213823e-05,
       -4.18171710e-03,  2.66683412e-02, -1.20736518e-02,  2.22610828e-02,
       -2.65497870e-02, -3.91703522e-02, -5.74009010e-02, -3.88294111e-03,
        1.25067756e-02, -4.24465367e-03,  1.70452116e-03,  3.90082625e-03,
       -5.74597300e-04, -5.69009239e-04,  2.54721121e-04,  7.97345315e-07,
       -1.51112448e-03,  8.42325894e-04, -1.36256903e-04, -1.96948198e-03,
        1.78420327e-03,  2.91789392e-03,  5.12670737e-04,  8.63499697e-04,
        1.65182605e-03,  2.41827089e-03,  2.99351279e-01, -3.13711424e-01,
        4.02070169e-04, -3.10498372e-03, -2.22086031e-03, -8.34959240e-04,
       -2.30402218e-03,  7.89729049e-03,  3.65504335e-03,  2.21623798e-03,
        2.38317738e-02,  3.09036913e-03,  1.19917229e-03, -7.38557316e-03,
        2.55481800e-02, -1.18374646e-02,  3.24375722e-03,  7.49950267e-03,
       -6.38689867e-04, -2.00575140e-05, -4.81615449e-04, -2.35131938e-03,
       -3.52249817e-02,  2.82288491e-02, -3.98197057e-03,  1.23972633e-03,
        2.02928753e-03, -2.51570621e-03, -2.36479095e-03, -2.38434222e-03,
        1.56391058e-03, -6.39294681e-01,  6.18371994e-01, -1.18278600e-03,
        4.77366733e-04, -3.25100832e-03,  2.16722132e-03, -6.69317518e-03,
       -8.85456655e-04, -7.89185146e-04, -2.22671459e-03,  1.06083344e-03,
        9.61487845e-04, -1.40655328e-04, -4.85394508e-03, -5.59674142e-03,
       -6.10906978e-04, -1.60933033e-02, -9.34249494e-03, -3.99461353e-04,
       -1.29580871e-03,  1.19729537e-03, -1.79962528e-03, -1.00341079e-03,
        2.89766855e-03,  3.33215552e-04,  1.72835203e-03,  9.42478632e-04,
        1.73285799e-03,  2.86989512e-03,  1.51888752e-03, -2.29610743e-03,
        1.03629474e-03,  9.30599720e-04, -8.98014237e-04,  1.74417298e-03,
        3.06266907e-04,  1.16975338e-03, -3.39435170e-03,  4.07941280e-04,
       -2.46785988e-03, -5.01559005e-04,  1.03388178e-03,  3.80903253e-04,
        1.81876051e-03, -1.38545149e-03, -7.58184084e-04,  1.19234297e-03,
       -3.57849193e-03,  3.41390243e-03, -2.78026568e-03,  1.41029785e-03,
        3.39873166e-03])), (0.0013616098195192291, array([ 9.53640582e-04, -7.13238024e-04,  3.86527564e-04, -1.14535389e-03,
        2.59888638e-03, -9.80247588e-04, -1.45060366e-04, -1.48602945e-03,
        6.95875943e-03,  8.87784189e-03,  1.50285362e-03, -2.97546665e-04,
        1.03623729e-03, -3.33873943e-04,  1.01963952e-01,  2.16283084e-01,
       -6.23038531e-01,  6.61434346e-02, -7.06786004e-03, -1.11571199e-03,
       -2.96068529e-04, -7.24238151e-02, -1.82810025e-01, -4.87148186e-02,
       -6.62404697e-02,  3.51050366e-01,  5.47066315e-01,  3.48546753e-02,
       -6.08526738e-02,  1.12099010e-03,  6.66540607e-03,  1.48291430e-02,
        3.37543683e-04,  1.62347123e-03, -2.62251676e-03, -5.62684405e-04,
        1.10172697e-03, -3.49570251e-04,  7.08906330e-04, -8.96795238e-04,
       -3.99647629e-04,  1.26462573e-03, -2.70562385e-03, -6.56953321e-04,
        2.08943881e-03,  1.04706511e-03,  2.98688416e-02, -3.46682063e-02,
       -1.91936073e-04,  1.63523298e-03,  1.28597855e-02,  9.82041675e-04,
        1.26295739e-02, -4.61543756e-02, -3.06081191e-02, -1.27836422e-03,
       -3.76256498e-02, -1.61808474e-02, -6.05068379e-02,  4.89838554e-02,
       -3.46463374e-02,  2.03669375e-02,  2.57162257e-02, -1.12785633e-03,
        2.51466959e-04,  7.80223056e-04, -5.57341604e-05,  1.61944051e-03,
        1.52434211e-01, -1.59180340e-01,  1.84687653e-04,  1.09839053e-03,
        7.26214830e-04,  4.63873748e-04,  3.40221006e-04, -8.27891052e-04,
       -8.90856362e-04, -6.31518080e-02,  6.52482616e-02, -1.29615644e-03,
       -6.75227373e-05, -2.74841430e-05,  6.70257405e-04, -1.24124847e-03,
       -2.92171109e-03,  1.85797111e-03, -3.36273172e-03, -2.61497727e-03,
       -2.24098701e-03,  2.93798783e-02, -3.36719647e-02,  1.91829449e-02,
        3.14623502e-03,  3.45786120e-02, -7.27931828e-04, -6.35430303e-04,
       -1.28312716e-04,  7.31993690e-04, -8.69106801e-04,  8.50527813e-04,
       -1.51863249e-03, -1.33467040e-03,  3.60143760e-04, -1.65723226e-03,
       -2.96704217e-04,  1.85794604e-04,  1.38402427e-04,  1.22990527e-03,
        1.49477978e-04, -1.93319497e-03, -1.23651586e-04,  4.74973575e-04,
        1.16819449e-03,  3.19268561e-03, -4.80486731e-04, -1.58178100e-03,
        6.32230104e-04, -5.56047241e-04, -1.37576282e-03, -7.95488586e-05,
       -5.43058197e-04,  5.38527386e-04, -3.97102807e-03,  2.15691582e-02,
       -2.13151810e-02, -2.56966884e-03, -8.72815795e-04,  2.26637114e-04,
        2.07740802e-03]))]
Eigenvalues in descending order: 
[9.498939889577516, 5.232019349425661, 4.648347996984388, 4.186731324975693, 3.562955057867467, 3.3781047188729665, 3.1234586983814996, 2.896740247012644, 2.720006508195297, 2.5190088116427725, 2.284114951260867, 2.1818983656529047, 2.026640067593797, 2.000264711286299, 1.9146976598067214, 1.8364540743240112, 1.794020828040592, 1.7671161813949274, 1.712986756405089, 1.6345009063066522, 1.6229130405662058, 1.5381237972906836, 1.4934920194863357, 1.4767495274172253, 1.4324278819833731, 1.4194538279849158, 1.3742373778836472, 1.3548182238607367, 1.2982610820213651, 1.2553872325196787, 1.2519010183004973, 1.1992625554851697, 1.1909340129758053, 1.1700726461917428, 1.154452314721701, 1.1461117799982323, 1.1367296683503523, 1.1058564927940202, 1.1005542519173042, 1.0843954603265749, 1.071502088995551, 1.0497410645241079, 1.0371823788216288, 1.0121977727831704, 1.0045224476827401, 0.9958082455692998, 0.9766471034002521, 0.9638690538465439, 0.9568034775608198, 0.9531393107346182, 0.9445335130824255, 0.9276787177698929, 0.9051945875625041, 0.9041279547015257, 0.8943292039526343, 0.8848149357993443, 0.8655011392416442, 0.860588941116157, 0.8454321663905576, 0.8338068301443086, 0.82174658607617, 0.8172270891494556, 0.8021328414712544, 0.8020812941732917, 0.7805066711509825, 0.7766602040861745, 0.7668348646315984, 0.7467798045103285, 0.7303328786065549, 0.7147643941394868, 0.702580147277707, 0.6757435455625513, 0.6695122731877595, 0.6384145453387995, 0.6312510279007324, 0.6195783099942663, 0.5775075493400974, 0.5605324571083408, 0.5313058458411763, 0.5185392641516556, 0.5058808666035016, 0.48815478102667087, 0.48091249851444784, 0.4705483856582529, 0.4520642506531529, 0.4434301613462223, 0.4316997399007819, 0.41284378457511933, 0.3979822468322802, 0.3732432045218797, 0.3539863436729204, 0.33288607654119334, 0.3118602467346556, 0.3107910612379128, 0.3027321350599863, 0.2853545325513171, 0.2821485621770654, 0.26759012455768594, 0.254600982853172, 0.2381671122787249, 0.2176831630585945, 0.20495277712210191, 0.19899277700790852, 0.190579637897479, 0.1634696686760357, 0.15551040347596667, 0.14468890444904617, 0.126178577427656, 0.11782802234971994, 0.11502707186036656, 0.09529335923366494, 0.08081511465511029, 0.07034818320620351, 0.05941550900554441, 0.051996011817034324, 0.03855213104402428, 0.033110401949095536, 0.030088627042818113, 0.026150384493922825, 0.01844063797448562, 0.014094693461094172, 0.01101529050517491, 0.006840307724769044, 0.004886419819025011, 0.0041759269748398485, 0.0035176937721913258, 0.0023348159473509968, 0.0015564153983103311, 0.0013616098195192291]

tot = sum(eigenvalues)
var_explained = [(i / tot) for i in eigvalues_sorted]  # an array of variance explained by each 
# eigen vector... there will be 129 entries as there are 129 eigen vectors)
cum_var_exp = np.cumsum(var_explained)  # an array of cumulative variance. There will be 129 entries with 129 th entry 
# cumulative reaching 100%


print('Cumulative Variance explained:\n' , cum_var_exp)

Cumulative Variance explained:
 [0.0735882  0.11412061 0.15013132 0.18256589 0.21016807 0.23633822
 0.26053562 0.28297664 0.30404851 0.32356325 0.34125827 0.35816141
 0.37386177 0.38935781 0.40419095 0.41841795 0.43231621 0.44600604
 0.45927654 0.471939   0.48451169 0.49642752 0.50799759 0.51943796
 0.53053496 0.54153146 0.55217766 0.56267343 0.57273105 0.58245652
 0.59215499 0.60144566 0.61067182 0.61973636 0.62867989 0.63755881
 0.64636504 0.6549321  0.66345809 0.67185889 0.68015981 0.68829214
 0.69632718 0.70416867 0.7119507  0.71966521 0.72723129 0.73469837
 0.74211072 0.74949468 0.75681198 0.7639987  0.77101123 0.7780155
 0.78494386 0.79179851 0.79850354 0.80517052 0.81172007 0.81817957
 0.82454563 0.83087668 0.8370908  0.84330452 0.84935109 0.85536787
 0.86130854 0.86709383 0.87275171 0.87828899 0.88373187 0.88896685
 0.89415355 0.89909935 0.90398964 0.90878951 0.91326346 0.9176059
 0.92172192 0.92573904 0.92965809 0.93343982 0.93716545 0.94081078
 0.94431292 0.94774817 0.95109254 0.95429084 0.957374   0.96026551
 0.96300784 0.96558671 0.96800269 0.97041038 0.97275565 0.97496629
 0.97715209 0.97922511 0.9811975  0.98304258 0.98472897 0.98631673
 0.98785833 0.98933475 0.99060115 0.99180588 0.99292679 0.99390429
 0.9948171  0.99570822 0.99644645 0.99707253 0.99761751 0.99807781
 0.99848062 0.99877928 0.99903579 0.99926888 0.99947147 0.99961433
 0.99972352 0.99980886 0.99986185 0.9998997  0.99993205 0.99995931
 0.99997739 0.99998945 1.        ]

# Plotting the Explained variance and principal components
plt.figure(figsize=(10,5))
plt.bar(range(1,130), var_explained, alpha=0.5, align='center', label='individual explained variance')
plt.step(range(1,130),cum_var_exp, where= 'mid', label='cumulative explained variance')
plt.ylabel('Explained variance ratio')
plt.xlabel('Principal components')
plt.show()

# From below we plot we can not see any particular elbow so lets try selecting number of dimensions based on the cumulative variance explained
# we can see that 75 features explain 90% of the data, so we will use first 75 principal components going forward and calulate the reduced dimensions.

# P_reduce represents reduced mathematical space....

x_reduce = np.array(eigvectors_sorted[0:75])   # Reducing from 129 to 75 dimension space

x_pca = np.dot(x_sc,x_reduce.T)   # projecting original data into principal component dimensions

x_reduced_pca = pd.DataFrame(x_pca)  # converting array to dataframe for pairplot

x_reduced_pca.head()

#Split into taining and testing data
X_train_sc, X_test_sc, y_train_sc, y_test_sc = train_test_split(x_reduced_pca, y, test_size=0.30, random_state=1)
X_train_sc.shape, X_test_sc.shape, y_train_sc.shape, y_test_sc.shape

((1096, 75), (471, 75), (1096,), (471,))

# Lets upsample the dataset to balance 
print("Before Upsampling, counts of label '-1': {}".format(sum(y_train_sc==-1)))
print("Before Upsampling, counts of label '1': {} \n".format(sum(y_train_sc==1)))

sm = SMOTE(k_neighbors = 5, random_state=1)   #Synthetic Minority Over Sampling Technique
X_train_sc_res, y_train_sc_res = sm.fit_sample(X_train_sc, y_train_sc.ravel())


print("After Upsampling, counts of label '-1': {}".format(sum(y_train_sc_res==-1)))
print("After Upsampling, counts of label '1': {} \n".format(sum(y_train_sc_res==1)))



print('After Upsampling, the shape of X_train_X: {}'.format(X_train_sc_res.shape))
print('After Upsampling, the shape of Y_train: {} \n'.format(y_train_sc_res.shape))

Before Upsampling, counts of label '-1': 1024
Before Upsampling, counts of label '1': 72 

After Upsampling, counts of label '-1': 1024
After Upsampling, counts of label '1': 1024 

After Upsampling, the shape of X_train_X: (2048, 75)
After Upsampling, the shape of Y_train: (2048,)

#Lets see if the performance of our model is increased or not


LR_model1 = LogisticRegression()
LR_model1.fit(X_train_sc_res,y_train_sc_res)

# Prediction on of test data
y_pred = LR_model1.predict(X_test_sc)
LR_model1_score = LR_model1.score(X_test_sc, y_test_sc)

# Let's measure the accuracy of this model's prediction
print('Accuracy of the Logistic regression model on Original data is', LR_model1_score*100)
#We can see that models performance is increased due to scaled values
print('\n\n Classification Report:')
print(metrics.classification_report(y_test_sc, y_pred))
print('\n\n Confusion Matrix:')
print(plot_confusion_matrix(LR_model1,X_test_sc,y_test_sc))

Accuracy of the Logistic regression model on Original data is 75.3715498938429


 Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.78      0.86       439
           1       0.10      0.34      0.16        32

    accuracy                           0.75       471
   macro avg       0.52      0.56      0.51       471
weighted avg       0.89      0.75      0.81       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECDA538C40>

Models overall accuracy have increased from 69% to 75% we can see that F1 score is increased by 5% for both -1 and 1 classes of prediction. Also as explained earlier, Precision nd recall for estimating -1 and 1 type of semi conductors have increased.

We can see that by using above techniques we have clearly increased out model's performance

# lets see if the performance of the mode changes when we change the sample population 
# We will create a method which will take independent variables, target variables and 2 ratios 
# based on which we will create 2 samples and run the model on both sample sets and see if the performance is changed 

from sklearn.pipeline import Pipeline 


def diff_sample_test (x,y,sample_ratio1,sample_ratio2):
    print ('shape of X and Y data set before applying enhancement is',x.shape,y.shape,'respectively')
    sm = SMOTE(k_neighbors = 5, random_state=1)   #Synthetic Minority Over Sampling Technique
    xsm,ysm = sm.fit_sample(x, y.ravel())
    
    xtrain1,xtest1,ytrain1,ytest1 = train_test_split(xsm,ysm,test_size = sample_ratio1 , random_state = 42)    
    xtrain2,xtest2,ytrain2,ytest2 = train_test_split(x,y,test_size = sample_ratio2 , random_state = 42)
    
    pipe_lr1 = Pipeline([('scl', StandardScaler()),('pca', PCA()), ('clf', LogisticRegression())]) 
    pipe_lr1.fit(xtrain1, ytrain1)
    print ('\n\n\nDetails of samples with', sample_ratio1,'ratio\n')
    print ('size of x train, y train, x test and y test samples is',xtrain1.shape,ytrain1.shape,xtest1.shape,ytest1.shape, 'respectively')
    print('Test Accuracy for above sample is: %.3f' % pipe_lr1.score(xtest1, ytest1))
    
    pipe_lr2 = Pipeline([('scl', StandardScaler()), ('pca', PCA()),('clf1', LogisticRegression())]) 
    pipe_lr2.fit(xtrain2, ytrain2) 
    print ('\n\n\nDetails of samples with', sample_ratio2,'ratio\n')
    print ('size of x train, y train, x test and y test samples is',xtrain2.shape,ytrain2.shape,xtest2.shape,ytest2.shape ,'respectively')
    print('Test Accuracy for above sample is: %.3f' % pipe_lr2.score(xtest2, ytest2))

diff_sample_test (x,y,0.20,0.90)
# We can see even with changing the sample sizes models performance is quite good,
# certainly scaling and dimension reduction enhances the data resulting in better model performances

shape of X and Y data set before applying enhancement is (1567, 129) (1567,) respectively


Details of samples with 0.2 ratio

size of x train, y train, x test and y test samples is (2340, 129) (2340,) (586, 129) (586,) respectively
Test Accuracy for above sample is: 0.840


Details of samples with 0.9 ratio

size of x train, y train, x test and y test samples is (156, 129) (156,) (1411, 129) (1411,) respectively
Test Accuracy for above sample is: 0.899

# Lets try using all the models and find the best one for our data set
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier

models = []
models.append(('GaussianNaiveBayes', GaussianNB()))
models.append(('DecisionTreeClassifier', DecisionTreeClassifier()))
models.append(('KNeighborsClassifier', KNeighborsClassifier()))
models.append(('SVC', SVC()))
models.append(('RandomForestClassifier', RandomForestClassifier()))
models.append(('BaggingClassifier', BaggingClassifier()))
models.append(('AdaBoostClassifier', AdaBoostClassifier()))
models.append(('GradientBoostingClassifier', GradientBoostingClassifier()))

Accuracy = []
recall = []
MName = []

for name, model in models:
    #stratified_kfold_CV_all = StratifiedKFold(n_splits = 10, random_state = 1)
    #StatifiedKF_results_all = cross_val_score(model, X_train_sc_res,y_train_sc_res, cv = stratified_kfold_CV)
    name = model.fit(X_train_sc_res,y_train_sc_res)
    #results.append(StatifiedKF_results_all)
    #names.append(name)
    #print("%s: %f (%f)" % (name, StatifiedKF_results_all.mean(), StatifiedKF_results_all.std()))
    # Prediction on of test data
    y_pred = name.predict(X_test_sc)
    score = name.score(X_test_sc, y_test_sc)
    Accuracy.append(score)
    MName.append(str(name))
    recall.append(metrics.recall_score(y_test_sc, y_pred))
    

# Let's measure the accuracy of this model's prediction
    print('\n\nAccuracy of the ', name,'model is', score*100)
#We can see that models performance is increased due to scaled values
    print('\nClassification Report:')
    print(metrics.classification_report(y_test_sc, y_pred))
    print('\n Confusion Matrix:')
    cm = plot_confusion_matrix(name,X_test_sc,y_test_sc)
    print (cm)
    plt.show()


Accuracy of the  GaussianNB() model is 53.71549893842887

Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.54      0.68       439
           1       0.07      0.50      0.13        32

    accuracy                           0.54       471
   macro avg       0.51      0.52      0.41       471
weighted avg       0.88      0.54      0.65       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD99952E0>


Accuracy of the  DecisionTreeClassifier() model is 77.07006369426752

Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.81      0.87       439
           1       0.09      0.25      0.13        32

    accuracy                           0.77       471
   macro avg       0.51      0.53      0.50       471
weighted avg       0.88      0.77      0.82       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD98D1E50>


Accuracy of the  KNeighborsClassifier() model is 45.22292993630573

Classification Report:
              precision    recall  f1-score   support

          -1       0.96      0.43      0.59       439
           1       0.09      0.78      0.16        32

    accuracy                           0.45       471
   macro avg       0.53      0.60      0.38       471
weighted avg       0.90      0.45      0.56       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD99952E0>


Accuracy of the  SVC() model is 93.41825902335457

Classification Report:
              precision    recall  f1-score   support

          -1       0.94      1.00      0.97       439
           1       0.60      0.09      0.16        32

    accuracy                           0.93       471
   macro avg       0.77      0.54      0.56       471
weighted avg       0.91      0.93      0.91       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD9F59640>


Accuracy of the  RandomForestClassifier() model is 92.1443736730361

Classification Report:
              precision    recall  f1-score   support

          -1       0.93      0.99      0.96       439
           1       0.14      0.03      0.05        32

    accuracy                           0.92       471
   macro avg       0.54      0.51      0.51       471
weighted avg       0.88      0.92      0.90       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD9F5E040>


Accuracy of the  BaggingClassifier() model is 87.04883227176221

Classification Report:
              precision    recall  f1-score   support

          -1       0.93      0.93      0.93       439
           1       0.06      0.06      0.06        32

    accuracy                           0.87       471
   macro avg       0.50      0.50      0.50       471
weighted avg       0.87      0.87      0.87       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECDA2D7550>


Accuracy of the  AdaBoostClassifier() model is 80.67940552016985

Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.84      0.89       439
           1       0.13      0.31      0.18        32

    accuracy                           0.81       471
   macro avg       0.54      0.58      0.54       471
weighted avg       0.89      0.81      0.84       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD9F59F10>


Accuracy of the  GradientBoostingClassifier() model is 86.62420382165605

Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.92      0.93       439
           1       0.12      0.16      0.14        32

    accuracy                           0.87       471
   macro avg       0.53      0.54      0.53       471
weighted avg       0.88      0.87      0.87       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECDA2D7430>

df = pd.DataFrame()
df['Model_Name'] = MName
df['Accuracy'] = Accuracy
df['Recall'] = recall
print (df)
plt.plot(Accuracy,'g*', label = 'Accuracy')
plt.plot(recall,'ro', label = 'Recall')
plt.legend()
plt.show()

                     Model_Name  Accuracy   Recall
0                  GaussianNB()  0.537155  0.50000
1      DecisionTreeClassifier()  0.770701  0.25000
2        KNeighborsClassifier()  0.452229  0.78125
3                         SVC()  0.934183  0.09375
4      RandomForestClassifier()  0.921444  0.03125
5           BaggingClassifier()  0.870488  0.06250
6          AdaBoostClassifier()  0.806794  0.31250
7  GradientBoostingClassifier()  0.866242  0.15625

We can see that SVC, RandomForestClassifier, BaggingClassifier,AdaBoostClassifier and GradientBoostingClassifier performs better with Accuracy more than 80%, however when it comes in inferencing how much conductors will fail KNN model is out performing all other models.

Hence with agreement with SME, If the agreement is for having highest accuracy in predicting correct conductors we can choose SVC model and if the agreement is for having a model which will prioritize identitfying fail conductors against identifying pass conductors, we will choose KNN model

So lets search for the best hyper parameter for above two models and see if the performance is enhanced or not

# Lets define hyper parameter 
# specify parameters and distributions to sample from
SVC_param = {"kernel" : ['linear', 'poly', 'rbf', 'sigmoid'],
              "gamma" : ['scale', 'auto']}

# run Grid search
grid_search = GridSearchCV(SVC(), param_grid=SVC_param)
grid_search.fit(X_train_sc_res,y_train_sc_res)
print('Parameters suggested for SVC as per Grid search CV', grid_search.best_params_)
print('Accuracy for best SVC as per Grid search CV', grid_search.best_score_)

Parameters suggested for SVC as per Grid search CV {'gamma': 'auto', 'kernel': 'rbf'}
Accuracy for best SVC as per Grid search CV 0.9921855805355119

KNN_param = {'n_neighbors': list(range(1,9)),
             'algorithm': ('auto', 'ball_tree', 'kd_tree' , 'brute') }

# run Grid search
knn_grid_search = GridSearchCV(KNeighborsClassifier(), param_grid=KNN_param)
knn_grid_search.fit(X_train_sc_res,y_train_sc_res)
print('Parameters suggested for Random Forest as per Grid search CV', knn_grid_search.best_params_)
print('Accuracy for best Random Forest as per Grid search CV', knn_grid_search.best_score_)

Parameters suggested for Random Forest as per Grid search CV {'algorithm': 'auto', 'n_neighbors': 2}
Accuracy for best Random Forest as per Grid search CV 0.8193464130240324

So we have best hyper parameters for both SVC and KNN models
Lets create both models individually and check the classificaiton report respectively for the future data set

supersvc = SVC(gamma= 'auto', kernel= 'rbf')
supersvc.fit(X_train_sc_res,y_train_sc_res)

# Prediction on of test data
y_pred = supersvc.predict(X_test_sc)
supersvc_score = supersvc.score(X_test_sc, y_test_sc)

# Let's measure the accuracy of this model's prediction
print('Accuracy of the SVC model is', supersvc_score*100)
#We can see that models performance is increased due to scaled values
print('\n\n Classification Report:')
print(metrics.classification_report(y_test_sc, y_pred))
print('\n\n Confusion Matrix:')
print(plot_confusion_matrix(supersvc,X_test_sc,y_test_sc))

Accuracy of the SVC model is 93.20594479830149


 Classification Report:
              precision    recall  f1-score   support

          -1       0.93      1.00      0.96       439
           1       0.50      0.03      0.06        32

    accuracy                           0.93       471
   macro avg       0.72      0.51      0.51       471
weighted avg       0.90      0.93      0.90       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECDAB8E3A0>

super_KNN = KNeighborsClassifier(algorithm= 'auto', n_neighbors =2)
super_KNN.fit(X_train_sc_res,y_train_sc_res)

# Prediction on of test data
y_pred = super_KNN.predict(X_test_sc)
super_KNN_score = super_KNN.score(X_test_sc, y_test_sc)

# Let's measure the accuracy of this model's prediction
print('Accuracy of the KNN model is', super_KNN_score*100)
#We can see that models performance is increased due to scaled values
print('\n\n Classification Report:')
print(metrics.classification_report(y_test_sc, y_pred))
print('\n\n Confusion Matrix:')
print(plot_confusion_matrix(super_KNN,X_test_sc,y_test_sc))

Accuracy of the KNN model is 66.45435244161358


 Classification Report:
              precision    recall  f1-score   support

          -1       0.94      0.69      0.79       439
           1       0.08      0.38      0.13        32

    accuracy                           0.66       471
   macro avg       0.51      0.53      0.46       471
weighted avg       0.88      0.66      0.75       471


 Confusion Matrix:
<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x000001ECD64CE490>

We can see that performance of SVC even after searching the best hyper parameters is maintained to 93%. However, performance for KNN model with hyper tuning is reduced w.r.t its prime motive of predicting fail conductors, hence we can choose not to hypertune KNN model and use it in automode completely.

Importing Future data set and predicting target variable for this data set

future_data = pd.read_excel('Future_predictions.xlsx')

future_data.shape

(18, 591)

future_data.isnull().sum().sum()

520

future_x = future_data.drop(labels = 'Time', axis = 1)

# Reducing to top 75 supporting features

RFmodel = RandomForestClassifier(bootstrap= True, criterion= 'gini', max_depth= None)
RFmodel.fit(x,y)
RFmodel.feature_importances_

array([0.01250188, 0.00454851, 0.01183385, 0.00427229, 0.00749766,
       0.00578184, 0.00845835, 0.00474512, 0.01213856, 0.00758788,
       0.00650453, 0.00616132, 0.00598408, 0.01353885, 0.00610327,
       0.00707871, 0.00343864, 0.00950082, 0.01052765, 0.00760384,
       0.02825403, 0.00658387, 0.00812971, 0.01224884, 0.02043785,
       0.01813056, 0.0055625 , 0.0125629 , 0.00728728, 0.01048659,
       0.00788983, 0.00682033, 0.0106956 , 0.00562919, 0.00750529,
       0.00726998, 0.0049119 , 0.00681966, 0.00515747, 0.00533573,
       0.00882911, 0.00745891, 0.00389374, 0.00842129, 0.00625526,
       0.00405232, 0.01011734, 0.00671884, 0.00498824, 0.00489368,
       0.00731516, 0.0040101 , 0.00473903, 0.00693572, 0.00580328,
       0.00771801, 0.00839137, 0.0105377 , 0.00905314, 0.0065213 ,
       0.00662652, 0.00977543, 0.01106865, 0.00597724, 0.00789563,
       0.00913616, 0.00619166, 0.00755363, 0.00768448, 0.00591651,
       0.00536885, 0.00777953, 0.00517977, 0.00354475, 0.01054586,
       0.00608641, 0.01500123, 0.01002834, 0.00866269, 0.00584775,
       0.00888806, 0.00946957, 0.00494864, 0.00679646, 0.00818519,
       0.01087528, 0.00781509, 0.00617515, 0.00508439, 0.00600275,
       0.00808108, 0.00472966, 0.01048855, 0.        , 0.00769813,
       0.00572246, 0.00598348, 0.0035884 , 0.00589246, 0.00620396,
       0.00679405, 0.00741007, 0.00446738, 0.00910528, 0.01139281,
       0.00666362, 0.00366073, 0.00645957, 0.0137689 , 0.0043151 ,
       0.00652834, 0.00870401, 0.00139057, 0.00910181, 0.0057988 ,
       0.00463887, 0.00507055, 0.00796042, 0.00841738, 0.0102278 ,
       0.01564326, 0.00972442, 0.00564035, 0.00884027, 0.01169355,
       0.01080784, 0.00440848, 0.00775433, 0.00493128])

imp_features = np.argsort(RFmodel.feature_importances_)[0:84]
future_x_reduced = future_x[imp_features]
future_x_reduced.head()

future_x_reduced.describe().T

# We can see that many columns have too many null values, so columns which have null values more than 70% can be droppped
# as majority of the data is missing imputing and creating majority of the artificial data is of no use in above case
# SO columns having null values more than roughly 1000 can be dropped
counter  = 0

for columns in future_x_reduced.columns:
    if future_x_reduced[columns].isnull().sum() >= 15:
        counter = counter + 1
        print( columns , 'Column has', future_x_reduced[columns].isnull().sum() , ' null values, hence can be dropped')
        del future_x_reduced[columns]
        

print('New shape of the data set after deleting above', counter ,' columns is', future_x_reduced.shape)

109 Column has 18  null values, hence can be dropped
110 Column has 18  null values, hence can be dropped
New shape of the data set after deleting above 2  columns is (18, 82)

counter  = 0
for columns in future_x_reduced.columns:
    if future_x_reduced[columns].min() == future_x_reduced[columns].median() and future_x_reduced[columns].max() == future_x_reduced[columns].median():
        counter = counter + 1
        print( columns , 'Column has single value ', future_x_reduced[columns].median(), ', hence can be dropped')
        del future_x_reduced[columns]
        

print('New shape of the data set after deleting above', counter ,' columns is', future_x_reduced.shape)

97 Column has single value  0.0 , hence can be dropped
42 Column has single value  70.0 , hence can be dropped
52 Column has single value  0.0 , hence can be dropped
49 Column has single value  1.0 , hence can be dropped
5 Column has single value  100.0 , hence can be dropped
114 Column has single value  0.0 , hence can be dropped
69 Column has single value  1.0 , hence can be dropped
New shape of the data set after deleting above 7  columns is (18, 75)

future_x_reduced.isnull().sum().sum()

27

# Since we do not really know much about each particular feature, we will fill the null values with the mean value 

#Replace blank valus with Nan values
future_x_reduced = future_x_reduced.replace('', np.nan)

#Replace the Null value with the mean value of each column
for columns in future_x_reduced.columns:
    mean = future_x_reduced[columns].mean()
    future_x_reduced[columns] = future_x_reduced[columns].fillna(mean)

future_x_reduced.isnull().sum().sum()

0

final_svc = SVC(gamma= 'auto', kernel= 'rbf')
final_svc.fit(X_train_sc_res,y_train_sc_res)
Future_PassFail_svc = final_svc.predict(future_x_reduced)
print ('Prediction on FUture data set with SVC model is\n',Future_PassFail_svc)

Prediction on FUture data set with SVC model is
 [-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]

final_KNN = KNeighborsClassifier()
final_KNN.fit(X_train_sc_res,y_train_sc_res)
Future_PassFail_knn = final_KNN.predict(future_x_reduced)
print('Prediction on Future data set with Random Forest model is\n',Future_PassFail_knn)

Prediction on Future data set with Random Forest model is
 [-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1]

Conclusion and improvisation:

FirstlyBy using techiniques like Scaling and reducing dimensions based on the importance can enhance models performance
We can drop or clean data set using many techiniques like, low - variance columns, columns with high number of null values, columns with same valur in all rows PCA and Random forest
Different CV techniques can give us the ratio of the models performance based on which we can infer the range of model's performance in production
Grid search can give us best parameter of the model for enhancing its accuracy, however it can not always be help full as we saw in our data set, if the objective is to identify faulty condutors then we should use KNN on automode instead of best parameters.
As a part of improvisations high efforts should be made while collecting the data, Quality of the data is the topmost factor of data science, in our case we had too many null values, vague columns, 90% of the data have presence of outliers and most of all, target column is completely imbalance. Such data requires huge data manipulations which eventually tempers the original data. Hence probability of Model not performing well in production increases.

	0	1	2	3	4	6	12	15	21	22	23	24	28	40	43	45	46	48	50	51	55	59	60	62	63	64	65	66	67	68	70	71	72	73	88	90	115	117	133	135	136	137	138	139	140	142	148	150	151	152	154	159	160	161	162	180	182	183	185	187	188	196	197	199	200	201	202	203	204	205	207	208	223	225	250	252	268	270	271	272	273	274	275	283	286	287	294	295	296	297	324	332	333	335	336	338	339	340	341	343	344	345	346	361	363	388	390	412	413	415	417	418	419	421	423	424	425	428	429	430	431	432	433	434	435	436	438	439	440	456	457	460	467	468	470	471	472	473	474	476	477	478	480	482	483	484	485	486	487	488	489	490	494	495	496	497	499	500	510	511	519	520	521	522	523	524	525	547	548	550	555	561	562	569	570	572	574	576	577	581	585	589	Pass/Fail
0	1.000000	-0.143840	0.004756	-0.007613	-0.011014	0.002270	0.010368	0.030675	-0.011075	0.032206	-0.046678	-0.014921	0.054548	-0.009150	-0.050244	0.015586	-0.006066	0.004240	-0.019867	-0.050817	-0.024957	-0.105184	-0.036578	0.008042	-0.045524	-0.017521	-0.019591	-0.000433	-0.023074	0.033390	-0.001325	-0.000247	-0.003495	0.008029	-0.029554	0.012107	0.023283	-0.000732	-0.007950	-0.070572	0.157770	0.160297	-0.021708	0.001720	-0.011107	-0.007485	0.000533	0.006545	-0.014213	0.003507	-0.009449	-0.037011	-0.003787	0.014089	0.063448	-0.002006	0.030302	-0.023634	-0.006813	-0.013009	-0.046557	-0.023397	-0.019154	-0.016829	-0.029920	0.000113	-0.017513	-0.024940	-0.027469	-0.032856	-0.029298	-0.038483	-0.023147	-0.015300	0.008180	-0.004230	-0.024374	-0.069232	0.155582	0.151401	-0.012819	0.004790	-0.011100	0.001747	-0.018155	0.003504	-0.038470	-0.009218	0.013929	0.060713	-0.037667	-0.022557	-0.016478	-0.017414	-0.023233	-0.022272	-0.024616	-0.023229	-0.031396	-0.025620	-0.036954	0.000941	-0.002220	-0.022630	-0.029094	-0.000282	-0.004340	0.007662	-0.022543	-0.007946	0.030384	-0.002017	0.016435	-0.001046	0.026659	-0.029941	-0.007224	0.004678	0.039119	-0.056194	-0.041317	-0.010828	0.062605	-0.064344	-0.065386	-0.064745	0.026556	0.032202	0.050793	0.015557	-0.006110	0.003927	0.021150	0.053943	-0.026841	-0.010854	0.013495	0.023699	0.004028	-0.019361	-0.031410	-0.029400	-0.036717	-0.011355	0.048705	-0.015355	-0.023708	-0.033258	0.076663	0.060053	-0.028320	-0.011895	-0.023695	-0.021992	0.002052	-0.017511	-0.051050	-0.019014	-0.062474	-0.018555	-0.039162	-0.011748	0.014244	0.003531	0.000986	-0.004357	-0.011434	0.027381	-0.089448	-0.075674	0.030640	0.037917	-0.009583	0.049862	-0.018953	0.013678	0.015206	0.013228	0.008601	-0.017120	0.023589	0.004174	-0.025141
1	-0.143840	1.000000	0.005767	-0.007568	-0.001636	-0.025564	0.034062	-0.087315	0.058155	-0.052664	-0.016047	-0.060066	-0.022847	0.027101	-0.019848	0.017935	0.007923	0.005573	0.004888	0.022586	0.017092	0.007505	-0.035006	-0.013760	0.038595	0.058949	0.054413	-0.020000	0.002643	0.018941	-0.026117	-0.018276	-0.012443	-0.001506	-0.007194	0.019660	0.029167	-0.009297	-0.034737	0.051456	-0.031967	-0.113532	-0.030106	0.009966	-0.001786	0.028495	0.001703	-0.044156	-0.097923	-0.004628	0.018289	0.037930	0.029999	0.037932	-0.005155	-0.030468	0.004351	-0.007852	0.005508	0.003371	0.056727	0.003240	-0.002687	0.009757	-0.001837	0.005695	0.008953	-0.006723	0.002100	0.007972	-0.006351	-0.066794	-0.021081	0.038764	0.010467	-0.003781	0.055382	0.041599	-0.035679	-0.110727	-0.020571	0.009278	-0.001783	0.001968	-0.093081	-0.004637	0.040675	0.033998	0.036893	-0.005201	0.048492	0.004168	-0.000372	0.011866	0.007823	0.009072	-0.005718	0.001792	0.008263	-0.008455	-0.057460	0.001949	0.006292	-0.016832	0.043958	0.016542	-0.003942	0.019433	0.005295	0.027474	0.027260	0.043736	-0.023114	0.003700	-0.007353	-0.092269	-0.001941	0.002079	0.001136	0.049959	0.049177	0.032311	-0.001006	0.050409	0.053605	0.052811	0.024841	-0.011154	0.020194	0.008012	0.001850	0.041014	0.039161	-0.029422	0.022069	0.014448	-0.050559	-0.016558	-0.009430	-0.018242	0.003229	0.002838	-0.059279	0.024601	-0.027730	-0.049265	-0.013099	0.011614	-0.003226	0.002316	0.010647	-0.020355	0.019295	-0.018646	0.015467	0.037509	-0.001513	0.005301	0.018804	0.032532	0.010311	-0.004022	0.042952	0.009682	-0.005029	-0.007876	0.068595	0.034569	0.070026	0.026976	-0.024335	-0.025492	0.037138	-0.025490	-0.009000	0.001753	0.001303	0.002570	-0.010145	0.054006	0.002273	0.044797	-0.002603
2	0.004756	0.005767	1.000000	0.298935	0.095891	-0.136225	0.018326	0.006115	0.044468	-0.029830	0.009276	-0.021149	-0.061725	-0.014761	0.050536	0.005874	0.010206	-0.096163	-0.008547	-0.016448	-0.008088	0.039710	0.039177	-0.013211	-0.028692	0.007603	0.014053	0.001573	-0.013251	-0.022889	0.002973	0.053692	0.008350	-0.003914	0.000507	0.040394	0.039054	0.021111	-0.015685	-0.010705	0.051600	0.047842	0.272222	0.021581	0.095947	0.006404	0.015360	0.003297	0.003101	0.018247	0.008429	0.015631	0.012170	-0.031203	-0.059200	-0.017295	-0.019236	-0.046624	0.016055	-0.020921	0.001435	-0.020661	-0.017141	-0.034084	-0.006726	-0.022558	0.002079	-0.010616	-0.002809	-0.019800	-0.009537	-0.029961	0.016448	0.047563	0.018057	0.015614	-0.086716	-0.022980	0.053480	0.046357	0.168825	0.029334	0.095882	0.016481	0.007158	0.018239	0.017878	0.013097	-0.028628	-0.056396	0.000057	-0.020073	-0.010751	-0.030948	-0.023989	0.013134	-0.003215	-0.008152	-0.019603	-0.010406	-0.035011	-0.006335	-0.001214	0.010095	0.054482	0.020565	0.015703	-0.124758	0.107609	0.028089	0.048093	-0.013755	0.021395	0.014566	-0.003459	0.007356	0.028664	0.029698	0.008704	0.023505	0.022954	0.018743	-0.018969	0.022694	0.025098	0.025229	-0.074651	-0.058467	-0.020019	-0.000669	0.021667	0.013232	-0.038047	-0.073040	0.004692	-0.034835	-0.007182	-0.033182	-0.052553	-0.045830	-0.023197	0.030657	-0.045635	-0.024090	-0.032611	-0.009574	0.012172	-0.004345	0.007753	0.021461	-0.023853	0.003173	-0.007174	0.014202	0.048647	0.043351	0.004401	-0.005940	0.017035	0.063829	0.062442	-0.023720	-0.020388	0.013516	-0.029512	0.014930	-0.005911	0.028152	-0.026891	0.017110	-0.004217	0.025862	0.013310	0.064282	-0.037070	-0.000518	0.001342	0.002592	-0.028705	-0.007054	0.015752	-0.032890	-0.000957
3	-0.007613	-0.007568	0.298935	1.000000	-0.058483	-0.685835	-0.028223	-0.013157	0.021111	-0.033141	-0.007973	-0.049605	0.096876	-0.006463	-0.023326	-0.031329	-0.043089	-0.021524	-0.056539	-0.012499	-0.016235	-0.020691	-0.025746	-0.007548	-0.032560	-0.026986	-0.028134	-0.027224	0.009517	0.017397	-0.015267	0.083641	-0.002320	0.009338	-0.016283	-0.007633	-0.002689	0.037765	0.000703	0.051198	0.021363	-0.006539	-0.206134	0.299731	-0.059719	-0.073778	0.008853	-0.045914	-0.005321	0.007937	0.013438	-0.051199	-0.076747	-0.010054	0.041761	0.070750	0.022531	-0.020704	-0.002026	0.008143	-0.029602	0.000590	0.009206	0.016457	0.007700	-0.006193	0.014087	0.004216	0.012983	-0.004443	0.016520	-0.020699	0.012984	-0.030584	-0.040013	0.039671	-0.132485	0.054943	0.009293	-0.010670	-0.329403	0.307743	-0.059730	0.008141	-0.004497	0.007916	-0.053258	-0.074885	-0.013636	0.041969	-0.026751	0.006240	0.012213	0.019793	0.013443	0.014130	0.012932	0.012014	0.000108	0.023550	-0.022470	0.015632	0.009595	0.009297	-0.035444	-0.047899	0.039578	-0.182968	0.009818	0.005119	0.024687	-0.003754	0.033285	0.009323	-0.018230	-0.005952	0.008997	-0.031119	-0.003746	-0.043869	-0.056951	0.053350	0.017265	-0.041670	-0.035809	-0.044270	-0.008226	0.021093	0.070096	-0.022014	-0.001728	-0.015767	-0.006049	0.014881	0.026988	0.018025	0.055073	0.000456	0.028147	-0.024778	-0.003181	0.008685	-0.072706	0.030013	0.026909	-0.054647	-0.010999	0.001386	0.066520	0.009441	-0.030502	-0.044634	-0.019733	0.011414	0.010987	-0.029903	0.008492	0.017704	-0.079904	-0.019613	-0.008699	-0.032959	-0.019964	-0.040648	0.031870	0.040860	-0.007854	-0.051045	-0.066744	-0.021563	-0.060972	0.014912	0.043271	0.046434	0.002231	0.007634	0.006822	0.008216	0.016438	-0.071311	0.026019	-0.080341	-0.024623
4	-0.011014	-0.001636	0.095891	-0.058483	1.000000	-0.074368	-0.002707	0.011435	0.008353	-0.007773	0.004969	-0.013636	0.007753	0.025844	-0.012074	0.057947	0.047234	-0.028014	0.029248	0.006774	-0.052408	-0.020577	-0.008334	0.049400	-0.004640	0.002791	0.006155	0.045359	-0.002948	-0.016026	0.032287	0.037397	0.004717	-0.002216	-0.006984	-0.013718	0.012677	-0.004841	-0.047460	-0.020532	0.010716	0.004287	0.193495	0.006466	0.999975	0.667794	0.000583	-0.034916	-0.002010	-0.001580	0.006871	-0.001934	-0.002558	-0.017138	-0.017504	-0.002981	-0.013424	-0.013929	0.008997	0.000571	0.009328	0.005597	-0.005887	0.001512	-0.000862	0.025928	0.008611	-0.000232	-0.003064	-0.006370	0.004245	-0.004920	-0.010286	0.012493	0.009458	-0.000519	-0.012915	-0.025923	0.013104	0.008211	0.223024	0.035670	0.999976	0.000674	-0.002031	-0.001547	-0.003336	-0.000639	-0.019247	-0.016190	0.010048	0.004425	-0.006581	0.000463	-0.002010	0.005443	0.002815	-0.003091	-0.003335	0.006442	-0.004209	-0.000780	-0.004176	-0.005553	0.023229	0.017901	-0.000674	0.028328	0.938416	0.683776	0.001143	-0.020321	-0.008230	0.001204	-0.032696	-0.002047	-0.006193	-0.005514	-0.009466	-0.004403	-0.003564	-0.010965	0.020404	-0.005190	-0.006646	-0.006298	0.024303	0.031524	0.040941	-0.002249	0.012034	0.010376	-0.000094	0.026172	0.002907	-0.000615	-0.004844	0.028104	0.014844	-0.015896	-0.005290	-0.000981	-0.024783	-0.031177	-0.027321	-0.013163	0.017334	0.021910	0.010670	0.021383	0.020535	-0.001803	-0.005038	-0.008903	0.002687	0.014237	0.004433	0.004379	-0.002970	-0.005096	-0.000064	-0.003555	-0.005631	0.008843	0.007522	0.001458	-0.012081	-0.011498	-0.046330	0.005220	-0.022289	-0.025806	0.041957	0.027696	0.005273	-0.012024	-0.012264	-0.012163	-0.004070	0.088167	-0.001616	0.050910	-0.013756
6	0.002270	-0.025564	-0.136225	-0.685835	-0.074368	1.000000	0.058982	0.039815	-0.021131	0.035590	0.013043	0.057480	-0.107400	0.014575	0.011003	0.029945	0.030622	0.026156	0.048818	0.053057	0.013190	0.057800	0.017097	0.053272	0.030469	-0.020330	-0.003449	0.037892	0.017830	-0.054700	0.034194	-0.098829	0.009504	-0.003609	0.042190	-0.004335	-0.024270	-0.035948	-0.014152	-0.034496	-0.025010	0.006138	0.192320	-0.418355	-0.073462	-0.160978	0.018170	0.079507	-0.010553	0.027789	-0.003361	0.045314	0.055111	-0.009556	-0.090384	-0.087154	0.028125	0.026140	0.002745	-0.022264	0.041824	0.009720	0.022308	0.012430	-0.023806	-0.015873	-0.032401	-0.019459	0.000543	0.048276	-0.003503	0.037775	-0.011004	0.012131	0.023796	-0.041630	0.094769	-0.040559	-0.022838	0.008511	0.168446	-0.428265	-0.073466	0.019484	-0.016178	0.027787	0.047652	0.051698	-0.002760	-0.093579	0.038605	0.013940	0.021404	0.015709	0.018597	-0.032709	-0.030489	0.008566	0.044915	-0.006197	0.044044	-0.001735	-0.003158	-0.010266	0.022900	0.031070	-0.041378	-0.104900	-0.056450	-0.254698	-0.043188	-0.003964	-0.062485	0.014584	0.036852	-0.017212	0.036069	0.006708	0.011353	0.043572	0.048542	-0.017815	-0.042839	0.043919	0.036880	0.043969	-0.014556	-0.041655	-0.089987	-0.002236	0.002068	-0.018401	0.005001	-0.046208	0.041540	0.002904	-0.041647	0.004549	-0.027779	0.066205	0.043670	-0.043555	0.090244	-0.016502	-0.042417	0.026113	0.022466	0.029242	-0.067299	-0.025759	0.030266	0.039039	0.019779	-0.012311	-0.024347	0.011930	-0.010517	-0.035984	0.060425	0.030146	0.007628	0.014542	-0.009547	0.025062	-0.012695	-0.043822	0.002541	0.048668	0.070215	0.016154	0.066665	-0.041082	-0.046295	-0.070722	0.017264	0.009292	0.007783	0.007409	-0.012342	0.042446	-0.039517	0.043777	0.016239
12	0.010368	0.034062	0.018326	-0.028223	-0.002707	0.058982	1.000000	-0.033933	-0.061068	0.084746	-0.039963	0.047038	0.004407	0.021971	0.027272	-0.033722	-0.020290	-0.011594	-0.017019	-0.017528	0.027789	0.022526	0.005014	-0.011988	-0.013395	-0.039679	-0.045253	0.008773	-0.003563	-0.009888	-0.012357	-0.030585	-0.021080	0.012966	0.037838	0.006241	-0.020492	0.022255	-0.007097	0.022016	-0.054659	-0.005752	-0.007246	-0.045257	-0.002487	-0.001815	0.560274	0.004327	-0.039126	0.559761	0.512987	-0.026613	0.024142	-0.016556	-0.013467	0.000753	0.007994	0.005434	0.018045	0.007281	-0.015951	-0.022183	0.001834	-0.012074	-0.020370	-0.055341	-0.041238	-0.027686	-0.012369	-0.007796	-0.022881	-0.021682	-0.006252	-0.017073	0.001551	0.022686	0.007846	0.006445	-0.053940	-0.015085	-0.020028	-0.046890	-0.002501	0.563988	-0.041237	0.559760	-0.028247	0.019128	-0.017705	-0.012163	-0.015421	-0.015646	0.001891	-0.008991	-0.005033	-0.034137	-0.029310	-0.008093	-0.006452	-0.019502	-0.026168	-0.001575	-0.001708	-0.010490	-0.015058	-0.005990	0.022736	-0.038032	-0.011440	-0.009102	-0.055692	-0.005958	0.008314	0.551234	0.009568	-0.036748	0.542941	0.050897	0.031105	-0.033226	-0.009461	-0.045017	-0.049532	-0.032291	-0.032221	-0.031404	0.004300	-0.002819	-0.054285	0.010837	0.019641	-0.003256	0.006161	-0.020666	-0.023138	-0.013665	-0.008107	-0.037111	-0.040754	0.028069	-0.008589	-0.025749	0.018050	0.014685	-0.038991	0.055404	0.025057	-0.015412	-0.014044	0.035711	0.053359	-0.017719	-0.022577	-0.008136	0.003849	-0.017645	-0.009372	0.000123	-0.001157	-0.011431	0.000133	0.001144	-0.022790	0.001711	0.015141	0.023988	0.019349	0.020488	0.059876	-0.004345	0.044070	-0.027928	0.015934	-0.007246	-0.000639	0.036757	0.032908	0.035743	0.031434	-0.038481	0.000523	-0.036720	-0.005969
15	0.030675	-0.087315	0.006115	-0.013157	0.011435	0.039815	-0.033933	1.000000	0.001786	-0.002054	-0.021383	0.022276	0.018212	0.006769	0.024793	-0.000303	0.018156	0.013455	0.024650	0.026860	0.005856	0.022178	0.034017	0.023657	-0.005630	-0.037096	0.019487	0.006020	0.009494	-0.037605	0.027683	-0.029459	0.007532	-0.001884	0.036382	0.019760	0.028784	-0.016883	-0.017014	0.001615	0.023437	0.034874	-0.022190	-0.018498	0.011280	-0.011268	-0.012967	0.147330	0.479135	-0.010243	-0.020257	-0.008665	-0.037638	0.020081	0.032913	-0.007347	0.029576	-0.000074	-0.005318	0.011640	0.021117	0.050464	0.021682	0.046948	0.065260	0.089866	0.116357	0.089023	0.060010	0.051900	0.051737	-0.016114	0.006374	0.011028	-0.007850	-0.014996	0.043453	0.011273	0.024291	0.042403	-0.017674	-0.022461	0.011275	-0.013094	0.470048	-0.010334	-0.009648	-0.036574	0.016652	0.037962	0.015857	0.027074	0.015430	0.024519	0.028727	0.119924	0.098197	0.039041	0.042530	0.035008	-0.010450	0.008070	0.019806	0.020726	0.017922	-0.000749	-0.014789	-0.020993	-0.002345	-0.010814	0.029856	0.005642	0.021455	-0.013376	0.252881	0.323039	-0.001929	0.000438	-0.003696	-0.012381	-0.026910	0.013698	0.048543	-0.013601	-0.012784	-0.016001	0.025876	-0.013699	0.029563	-0.011621	-0.006639	-0.000904	0.002745	-0.010736	0.023597	0.064624	0.041329	0.073615	0.060787	0.017147	0.042255	0.134922	0.011658	-0.036588	-0.030424	0.002007	0.005349	-0.017061	-0.003285	0.001814	-0.006037	-0.019630	0.020435	0.003807	0.013484	0.007563	0.010213	-0.035410	0.000543	-0.000151	-0.002565	0.005861	0.000592	-0.012267	-0.000763	-0.013476	-0.005306	0.030121	0.019602	-0.023780	-0.011686	-0.044469	-0.047842	0.000864	0.011928	-0.024789	-0.024032	-0.023509	-0.019152	0.014444	0.017745	0.009764	-0.002884
21	-0.011075	0.058155	0.044468	0.021111	0.008353	-0.021131	-0.061068	0.001786	1.000000	-0.725921	0.159658	-0.179501	0.006597	-0.059722	-0.032037	0.024051	0.017270	0.040711	0.020040	0.048990	0.050986	-0.026307	-0.031716	-0.048142	0.043861	0.112512	0.088935	-0.036556	-0.021296	0.096364	-0.020642	0.043387	-0.014032	-0.008551	-0.023405	-0.005159	-0.004830	0.006299	-0.024727	-0.071701	0.054571	0.032744	0.031678	0.104564	0.008121	0.065895	-0.022747	0.001988	0.000161	-0.028551	0.004485	0.537685	0.348222	-0.056155	0.174701	-0.023982	-0.044969	-0.101018	-0.024700	-0.031557	0.026143	0.003100	-0.009594	-0.011525	-0.013941	0.036990	0.023331	-0.033409	-0.020295	-0.019787	-0.023672	-0.074772	-0.022357	0.031185	0.032908	0.012150	-0.006481	-0.067282	0.062480	0.047982	0.046031	0.101713	0.008124	-0.024465	0.001971	-0.028447	0.564821	0.383166	-0.043329	0.173248	0.027190	0.003765	-0.011920	-0.005022	-0.016453	0.018371	-0.024605	-0.024395	-0.023281	-0.017785	-0.065312	-0.029391	-0.025035	-0.025437	0.043785	0.049078	0.012374	0.104626	0.017795	0.060898	0.010411	0.020930	0.015172	-0.019949	0.034615	-0.003335	-0.026842	0.018747	-0.018220	0.674305	0.595582	0.103016	0.289180	0.631860	0.644550	0.638651	0.123154	0.121289	0.329713	-0.020014	-0.030285	-0.020806	0.039827	-0.022726	-0.013896	-0.009112	-0.057252	-0.004826	0.013894	-0.044566	-0.022998	0.010291	-0.093628	0.020485	0.014588	-0.104220	0.032174	0.006633	0.057521	0.022429	-0.043564	-0.056495	-0.029089	-0.021790	0.029570	0.031968	0.027578	-0.018658	0.059851	-0.013919	0.050050	0.000562	0.041535	0.033163	-0.006128	0.012397	0.111198	-0.031718	-0.059298	0.022100	-0.080022	-0.044959	0.025136	0.029445	-0.006139	0.025659	0.021480	0.024945	0.045633	-0.013449	-0.002637	-0.030761	0.107997
22	0.032206	-0.052664	-0.029830	-0.033141	-0.007773	0.035590	0.084746	-0.002054	-0.725921	1.000000	-0.152008	0.119966	-0.065717	-0.011674	0.004048	-0.060399	-0.060646	0.002082	-0.050837	-0.087362	-0.062155	0.053719	-0.016163	0.017979	-0.019988	-0.098182	-0.080591	-0.011172	0.030139	-0.046192	-0.014500	-0.067758	-0.023307	0.038281	0.021504	-0.002485	-0.026019	-0.008688	0.069178	0.032525	-0.054530	-0.019726	-0.018266	-0.095056	-0.007392	-0.062222	0.021957	-0.028780	-0.014156	0.031246	-0.010564	-0.474183	-0.032147	0.068315	-0.121344	-0.012279	0.001181	0.138990	0.041373	0.037281	-0.037121	0.009469	0.018839	0.025783	0.025976	-0.066247	-0.031094	0.060757	0.030179	0.041623	0.038784	0.077616	-0.004109	-0.019202	-0.031776	-0.010180	0.004968	0.032574	-0.064588	-0.034439	-0.049450	-0.091738	-0.007398	0.023723	-0.014008	0.031053	-0.503996	-0.072832	0.055953	-0.121697	-0.039289	0.010451	0.022220	0.020763	0.033337	-0.021042	0.049336	0.032827	0.042549	0.031286	0.067427	0.039877	0.037181	-0.003090	-0.036932	-0.052504	-0.010314	-0.080914	-0.026764	-0.061154	-0.086027	-0.017114	0.006897	0.017954	-0.028383	-0.016099	0.036357	-0.011456	0.006257	-0.622276	-0.448356	-0.086645	-0.234864	-0.619117	-0.637909	-0.631590	-0.078324	-0.043789	-0.184229	0.025419	0.047804	0.058807	-0.027899	-0.027977	-0.013318	0.021965	0.033488	-0.036004	-0.045496	0.055557	0.042009	-0.001623	0.118468	-0.019852	-0.025882	0.127448	-0.035974	0.026394	-0.075369	0.024625	0.039211	0.042621	-0.001649	-0.003711	-0.071921	-0.019411	-0.012566	-0.008783	-0.003432	0.045000	-0.059908	-0.039520	-0.053415	-0.028540	0.013016	-0.011389	-0.116045	0.077722	0.102231	-0.012161	0.090316	0.000750	-0.066227	-0.012544	-0.037876	0.005278	0.007127	0.009047	-0.021553	-0.030599	-0.008558	0.004419	-0.073380
23	-0.046678	-0.016047	0.009276	-0.007973	0.004969	0.013043	-0.039963	-0.021383	0.159658	-0.152008	1.000000	0.030027	-0.033922	0.018341	-0.016951	0.004805	-0.003082	0.008081	-0.003634	0.043283	0.004740	0.003601	-0.003600	-0.009536	-0.006910	0.006625	-0.028626	0.007543	-0.015536	0.027092	0.000672	0.027316	-0.006201	-0.001316	-0.005920	-0.034757	-0.040329	0.007194	-0.021529	0.002384	-0.030234	-0.061007	0.005422	-0.022332	0.004777	0.007691	-0.008614	0.012295	-0.035153	-0.007955	-0.004024	0.186806	0.122487	0.131162	-0.008482	0.002497	-0.010564	-0.003522	-0.028203	-0.021234	-0.004772	-0.025396	-0.024997	-0.028010	-0.036571	-0.028765	-0.039562	-0.030418	-0.030451	-0.032935	-0.021646	0.022051	-0.021149	-0.039061	-0.039557	0.001351	0.029825	0.010905	-0.027880	-0.055238	0.007182	-0.022683	0.004780	-0.008303	-0.038171	-0.008049	0.192170	0.128940	0.131281	-0.003344	0.007503	-0.018804	-0.018072	-0.020411	-0.028763	-0.042651	-0.036117	-0.024959	-0.030366	-0.017426	0.024759	-0.001822	-0.006510	-0.026550	-0.028019	-0.028017	0.001379	-0.012769	0.021769	0.006275	0.008062	-0.004809	-0.000203	-0.008222	-0.003494	-0.028234	0.002568	0.010192	0.015835	0.205401	0.181848	0.206027	0.027723	0.187554	0.189297	0.188968	-0.014048	-0.026998	-0.020916	-0.046035	-0.027501	-0.073994	0.014066	0.006899	-0.025416	-0.033368	-0.002071	-0.018494	-0.006206	0.007363	-0.028328	-0.042092	0.014797	0.020173	-0.023226	0.028445	-0.010144	0.029830	0.005254	-0.015039	0.020725	0.003559	0.038614	-0.019698	-0.007710	-0.036406	-0.010763	0.041259	-0.002977	0.001680	-0.006043	-0.017718	-0.030645	-0.033027	-0.020555	0.000466	0.028272	0.006352	-0.002404	-0.014331	0.025768	-0.033960	0.015186	0.007529	-0.025599	0.024716	0.025928	0.026589	0.010524	-0.014964	-0.002240	-0.037950	0.012873
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40	-0.009150	0.027101	-0.014761	-0.006463	0.025844	0.014575	0.021971	0.006769	-0.059722	-0.011674	0.018341	0.037691	0.179450	1.000000	0.011736	0.000789	0.010040	-0.031465	-0.007287	-0.007507	-0.038257	-0.123669	0.029032	-0.027646	-0.058522	-0.002978	-0.001990	0.005880	-0.032013	-0.006253	-0.009291	0.024986	-0.045716	0.013473	0.029112	0.051416	0.037168	0.004932	-0.040153	0.013172	-0.047593	-0.045268	-0.024112	0.007260	0.025617	0.007376	0.011604	-0.044008	0.018070	0.011685	0.006184	-0.078373	-0.119653	0.001898	-0.056792	0.067088	0.013702	-0.015752	-0.012522	0.014686	-0.029695	-0.018834	-0.006856	-0.010715	-0.002149	0.035778	0.021937	-0.001283	-0.015610	-0.041295	-0.033606	-0.066632	0.024508	-0.021316	-0.031408	0.011143	-0.011001	0.008672	-0.042335	-0.056588	-0.018228	0.002796	0.025628	0.011485	0.019332	0.011719	-0.084087	-0.126309	-0.000500	-0.048942	-0.034530	-0.035152	-0.013308	-0.027820	-0.024614	0.017356	-0.002221	-0.017152	-0.037174	-0.045647	-0.066232	-0.013378	-0.001055	0.023145	-0.036621	-0.043985	0.010812	0.001621	0.021383	0.008526	0.037595	0.048618	-0.027468	0.011183	-0.021849	0.018274	0.008281	0.043530	0.003159	-0.084906	-0.103825	0.000492	0.068195	-0.075646	-0.080117	-0.080122	0.036777	0.016402	0.024469	0.010756	-0.015541	-0.032645	0.011882	0.077859	-0.002950	0.013957	0.006459	0.023937	0.021728	-0.040955	-0.041405	0.013580	-0.075256	0.002277	0.076991	-0.016163	0.024071	-0.013448	0.088524	-0.055407	0.022887	-0.029484	0.022778	0.021191	0.035875	-0.026218	0.040731	0.011819	-0.091556	-0.030772	-0.049638	-0.044590	-0.024616	-0.033126	-0.035764	0.008840	-0.016520	-0.073297	-0.080793	-0.067912	-0.055879	0.014917	0.030533	-0.065979	0.021817	-0.007765	-0.011742	-0.008707	0.014344	-0.050235	0.007846	-0.060163	-0.051763
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59	-0.105184	0.007505	0.039710	-0.020691	-0.020577	0.057800	0.022526	0.022178	-0.026307	0.053719	0.003601	0.036681	-0.257863	-0.123669	0.070535	-0.278185	-0.220414	0.127696	-0.134897	0.020833	0.109279	1.000000	-0.077779	0.269070	0.246304	-0.324840	-0.220730	-0.002241	0.475908	-0.244242	0.085513	-0.144711	-0.104373	0.209048	0.054567	-0.089473	-0.042449	-0.031195	0.024350	-0.031325	0.013222	0.050251	0.082232	-0.065222	-0.020111	-0.001554	-0.000476	0.046847	0.004646	0.002711	-0.001580	0.058518	0.096572	0.008215	-0.104611	-0.173405	-0.029780	-0.001583	0.041538	0.002716	0.056263	0.406847	0.426106	0.376346	0.124026	-0.057769	0.121418	0.255745	0.389961	0.503903	0.418970	0.113690	0.006188	0.036629	-0.006035	-0.019943	-0.038009	-0.031151	0.034662	0.062221	0.038190	-0.061082	-0.020114	-0.001047	0.001340	0.002564	0.067321	0.096388	0.007035	-0.108761	0.052876	0.467771	0.439292	0.444914	0.463237	0.136256	0.209170	0.411837	0.497591	0.445788	0.121360	0.342251	0.299728	-0.002506	0.053651	0.003619	-0.019968	-0.037440	0.024593	-0.012237	-0.101886	-0.035252	-0.037902	-0.000935	0.070924	0.004922	0.028043	0.012259	-0.013562	0.072240	0.095420	0.013395	-0.072867	0.091144	0.085272	0.084576	-0.123167	-0.143582	-0.183498	0.003316	0.034839	0.055551	-0.030768	-0.220185	0.281393	0.232796	0.052424	0.070786	0.259023	0.317546	0.508747	-0.015302	0.207244	-0.003168	-0.107968	0.083781	-0.056382	-0.032892	-0.133053	0.035601	-0.021939	0.019338	0.040790	0.005247	0.019295	0.046344	-0.070973	-0.004101	0.217680	0.047969	0.154956	-0.012836	0.004569	0.001116	0.279940	-0.001897	0.064498	0.099669	0.152182	0.127545	0.105778	0.013609	-0.056697	0.008282	-0.065988	-0.015305	-0.014918	-0.005801	-0.023120	-0.024339	-0.027998	0.042628	0.155771
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71	-0.000247	-0.018276	0.053692	0.083641	0.037397	-0.098829	-0.030585	-0.029459	0.043387	-0.067758	0.027316	-0.022218	0.199896	0.024986	0.010402	0.083850	0.079939	-0.032439	0.066666	0.033012	-0.016165	-0.144711	0.037973	0.036950	-0.074165	0.039424	-0.015012	0.089578	-0.018667	0.096551	0.100363	1.000000	0.063917	-0.032177	-0.021769	0.011844	0.013614	0.013283	-0.034624	-0.018851	0.010784	-0.008039	-0.017426	0.146850	0.037182	0.098070	-0.029568	0.012607	0.014791	-0.031224	-0.014344	-0.020151	-0.027241	-0.008095	0.041955	0.075447	0.061474	-0.076573	0.011797	0.003978	0.006596	-0.013021	-0.028025	-0.010375	-0.063791	0.058222	0.027014	-0.037142	-0.013623	-0.116219	-0.044966	0.002162	0.044696	0.027842	0.067506	0.008916	-0.051976	-0.025893	0.004246	-0.007188	0.007727	0.143996	0.037183	-0.029852	0.013272	-0.031039	-0.022856	-0.025030	-0.008239	0.038765	0.010599	-0.020703	-0.026481	-0.014645	-0.031670	0.014915	-0.026572	-0.014407	-0.098599	-0.040898	-0.008428	-0.054283	-0.058492	0.050349	0.028697	0.065318	0.009064	0.089587	0.042840	0.107121	0.106211	0.020543	0.007676	-0.028561	0.023670	0.026741	-0.027372	-0.017988	0.007888	-0.016175	-0.021109	0.006453	0.035456	-0.017062	-0.013915	-0.011999	0.083423	0.132329	0.125694	0.039301	0.010528	-0.028309	-0.020434	0.048486	-0.009761	-0.001453	0.010799	0.051743	0.095394	-0.361232	-0.093707	0.002393	-0.490215	-0.008445	0.089972	-0.050822	0.000812	-0.037842	0.096241	0.000910	0.023133	-0.039816	-0.017795	0.043826	0.052648	0.028627	0.028286	-0.020503	-0.108340	0.013317	-0.042636	0.054947	0.023863	0.065276	-0.032929	0.005536	0.044974	-0.088584	-0.163120	-0.074789	-0.150207	0.038759	0.046400	0.020337	-0.000560	-0.047903	-0.047373	-0.047310	-0.039950	0.084174	-0.000381	0.040782	-0.002040
72	-0.003495	-0.012443	0.008350	-0.002320	0.004717	0.009504	-0.021080	0.007532	-0.014032	-0.023307	-0.006201	-0.006426	0.057796	-0.045716	0.105019	0.245773	0.285998	-0.077259	0.258487	0.004441	0.128248	-0.104373	0.211912	0.172162	-0.151485	-0.049238	-0.053350	0.308324	-0.158840	-0.045284	0.268947	0.063917	1.000000	-0.836166	0.009470	-0.011794	-0.036058	-0.005540	-0.004867	-0.026274	0.010469	0.036097	-0.050097	0.035917	0.004632	0.032132	-0.001209	0.000942	-0.012542	-0.000668	-0.000830	-0.028595	-0.029918	0.005586	-0.001104	0.068155	0.093459	-0.105885	0.000865	-0.024691	0.019261	-0.108976	-0.100367	-0.118456	-0.102635	0.020233	-0.068171	-0.136655	-0.179639	-0.154168	-0.131945	-0.066236	0.005683	-0.029366	-0.014372	-0.005376	0.110703	-0.024329	0.007138	0.037390	-0.025706	0.039614	0.004632	-0.002118	-0.019441	-0.000515	-0.029140	-0.030728	0.005325	-0.002478	0.018076	-0.117133	-0.107375	-0.126860	-0.134277	-0.082448	-0.129677	-0.183598	-0.161590	-0.128455	-0.080037	-0.648162	-0.690427	0.002271	-0.022838	-0.012571	-0.005335	0.029969	-0.012577	0.029580	0.032003	0.021777	0.001038	-0.001394	-0.030783	-0.011010	-0.009410	0.032089	0.019466	-0.033985	-0.036968	0.007266	0.037337	-0.037537	-0.036266	-0.035614	0.007113	0.006522	0.061297	0.040362	0.003333	0.022586	-0.082685	0.034176	0.002414	-0.116452	0.010241	0.060585	-0.004313	-0.106494	-0.146223	-0.051166	-0.086596	-0.015759	-0.012460	-0.074045	0.019429	-0.045127	0.046552	-0.042627	-0.000981	-0.020809	-0.003971	0.004298	0.047276	-0.027539	0.013933	0.032638	-0.029342	0.006868	-0.079278	-0.031229	0.012761	-0.008664	-0.032785	-0.007425	0.009633	-0.061840	-0.093288	-0.003458	-0.029486	-0.029320	0.012855	0.022521	0.491605	-0.095208	-0.096756	-0.131314	-0.081573	0.032663	0.004822	0.003765	0.006368
73	0.008029	-0.001506	-0.003914	0.009338	-0.002216	-0.003609	0.012966	-0.001884	-0.008551	0.038281	-0.001316	-0.004177	-0.001064	0.013473	0.054849	0.003708	0.027790	-0.023410	0.030676	-0.003372	-0.012185	0.209048	-0.017719	0.218184	0.159073	-0.011929	0.006939	0.081726	0.314406	-0.046426	0.166226	-0.032177	-0.836166	1.000000	-0.041857	-0.019650	0.019446	-0.008114	-0.028547	0.033957	-0.032520	-0.064716	0.032769	-0.020849	-0.002341	-0.031182	0.002232	-0.037657	0.023865	0.000568	0.006776	-0.021455	-0.017077	-0.028449	0.010387	-0.028237	0.001787	0.075979	0.006211	0.016386	-0.040513	0.282297	0.294067	0.267204	0.134320	0.029426	0.148381	0.230348	0.301408	0.328138	0.314853	0.106922	-0.007030	0.004452	-0.005722	-0.007868	-0.035730	0.045462	-0.039740	-0.068706	0.011421	-0.022806	-0.002341	0.002636	0.031669	0.000457	-0.022890	-0.017665	-0.028225	0.010194	-0.045405	0.318970	0.302504	0.310829	0.320255	0.159851	0.214666	0.306255	0.332633	0.327783	0.124389	0.826890	0.843282	-0.001097	-0.005636	-0.011320	-0.007961	-0.026726	-0.000354	-0.027307	-0.002059	0.007127	-0.012028	0.002889	-0.030977	0.022648	0.007887	-0.011644	-0.015647	-0.020557	-0.021013	-0.024637	-0.028659	-0.020926	-0.020926	-0.021457	-0.006062	-0.003693	-0.025871	-0.025389	0.008098	-0.051501	-0.029035	0.002429	0.187957	0.188040	0.109593	0.011225	0.162740	0.180487	0.321769	0.062260	0.123881	0.017323	0.001505	0.071334	-0.039152	0.058191	-0.026661	0.046873	0.004458	0.004965	-0.016310	-0.004172	-0.011455	0.005391	-0.021484	-0.025772	-0.004625	0.014414	0.049264	0.010971	-0.010481	-0.010426	0.145970	-0.002323	-0.003989	0.060142	0.035053	-0.000800	0.021236	0.042744	0.020250	-0.010647	-0.505227	0.106508	0.108690	0.143273	0.092446	-0.023746	-0.003146	-0.001901	-0.016896
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148	0.000533	0.001703	0.015360	0.008853	0.000583	0.018170	0.560274	-0.012967	-0.022747	0.021957	-0.008614	0.013058	0.003631	0.011604	0.009375	0.014087	0.009143	-0.045813	-0.004425	-0.016348	0.007789	-0.000476	-0.002557	0.022310	-0.020512	-0.016573	-0.014626	0.018261	-0.000284	-0.012846	0.012909	-0.029568	-0.001209	0.002232	0.041787	-0.009009	0.007212	-0.001214	-0.010975	0.036622	-0.020231	-0.016887	-0.024656	0.009371	0.000385	0.004713	1.000000	-0.003207	-0.001053	0.991069	0.939605	-0.002958	0.001619	-0.019094	-0.019313	0.034211	0.013515	0.038355	0.012251	0.011088	-0.030771	-0.004234	0.001672	0.002519	-0.004471	-0.014512	-0.003045	0.008999	0.001785	0.004380	0.003522	0.035166	-0.018533	-0.023933	-0.016990	-0.005163	0.047886	0.017669	-0.021174	-0.019127	-0.035213	0.011367	0.000377	0.999522	0.000087	0.991077	-0.003022	0.002106	-0.018789	-0.019892	-0.028234	-0.003364	0.003011	0.000360	0.003385	-0.000858	0.006763	0.000982	0.005504	0.001773	0.033410	0.003624	0.004008	-0.019980	-0.016656	-0.012807	-0.005096	0.002115	0.013300	0.002023	-0.022812	0.012345	0.004838	0.998815	-0.008378	-0.000811	0.961851	-0.000371	0.001079	-0.001508	0.000742	-0.018986	-0.026283	-0.000233	0.000688	0.001038	-0.008192	-0.019074	-0.033953	-0.003010	0.016521	-0.032138	0.001269	-0.008974	-0.000226	0.002149	0.027785	-0.015693	-0.009761	0.046484	0.003871	0.004726	0.068869	-0.026608	-0.016829	0.027459	0.000901	-0.027384	-0.011047	-0.006471	0.038801	-0.040621	-0.003227	-0.019236	0.040603	-0.023419	-0.026233	0.017254	-0.000825	-0.022882	-0.001923	-0.009680	-0.004119	-0.018990	-0.001530	-0.005876	-0.010883	0.009896	0.029617	0.002979	0.017951	-0.013085	-0.000454	-0.001095	0.001240	-0.001970	-0.001289	-0.002053	0.010378	-0.000877	0.005805	-0.025198	-0.005603
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151	-0.014213	-0.097923	0.003101	-0.005321	-0.002010	-0.010553	-0.039126	0.479135	0.000161	-0.014156	-0.035153	0.015927	-0.011084	0.018070	0.002687	-0.012411	-0.000522	0.037932	0.010521	0.036693	-0.014659	0.004646	0.004752	-0.002901	0.027861	0.005239	0.105942	-0.066671	0.026300	-0.018791	-0.003420	0.014791	-0.012542	0.023865	0.004029	-0.013747	0.014209	0.001873	-0.021337	-0.006195	-0.000723	0.027193	0.006185	0.010711	-0.002110	-0.017621	-0.001053	0.019036	1.000000	-0.001351	-0.000212	-0.037768	-0.041481	-0.022711	-0.001912	-0.019413	0.007480	0.034821	-0.014380	0.024594	0.060235	0.154703	0.084580	0.162162	0.210872	0.229005	0.342114	0.291684	0.180251	0.085854	0.168264	-0.044176	-0.004567	-0.027003	-0.013968	-0.002102	0.004401	0.007681	-0.007574	0.019428	0.011212	0.006468	-0.002111	-0.001478	0.990003	-0.001252	-0.037985	-0.039225	-0.024100	0.003523	0.058136	0.092687	0.055130	0.100828	0.088353	0.356976	0.333850	0.116306	0.073999	0.124197	-0.044927	0.076543	0.104583	0.014729	-0.011899	-0.004947	-0.001864	0.009520	-0.004279	-0.015024	-0.001536	0.014553	0.020663	-0.000851	0.204007	0.976231	-0.006952	-0.009331	0.004256	-0.023682	-0.027869	-0.025503	0.015719	-0.013987	-0.012405	-0.011908	0.003535	0.010183	0.011744	-0.015189	-0.016789	0.020694	-0.010144	0.004193	0.052058	0.226868	0.080051	0.139597	0.174912	0.009372	0.069115	0.409063	-0.029500	-0.038906	0.010116	-0.030072	0.048521	-0.012468	-0.055680	-0.011290	0.003319	0.002930	0.008267	-0.004422	0.023573	-0.024888	0.049077	-0.011575	-0.029362	0.025945	-0.018408	-0.000567	0.027825	-0.015636	-0.011052	-0.002071	0.008532	-0.009725	0.002410	-0.001601	-0.019500	0.006152	-0.003437	-0.020740	0.007083	-0.019926	-0.020553	-0.020550	-0.008026	0.001699	0.000615	0.052482	-0.022415
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499	-0.051050	-0.001513	0.004401	0.008492	0.004433	-0.010517	-0.009372	0.010213	0.027578	-0.012566	-0.010763	-0.016048	-0.000872	0.040731	0.016957	-0.004412	0.004000	-0.004169	0.006043	0.039949	0.007146	-0.070973	0.020793	-0.028805	0.009342	0.019329	0.016352	-0.010435	-0.015427	-0.020866	-0.021870	0.028286	0.013933	-0.021484	-0.042354	0.007197	0.025306	-0.018355	0.002640	0.018569	-0.033258	-0.047762	0.020568	-0.018994	0.004507	-0.015738	-0.026233	-0.003352	0.049077	-0.020179	-0.037648	0.012746	-0.000969	-0.054023	0.007257	0.049631	0.015053	-0.002694	-0.020959	-0.020758	0.051907	-0.000061	0.006263	0.007543	0.020659	0.006491	0.013697	0.008104	0.001471	-0.021073	-0.008652	0.009127	-0.001025	-0.010748	-0.012646	-0.020483	0.026156	-0.005824	-0.031614	-0.058012	0.025200	-0.018638	0.004499	-0.026926	0.049145	-0.020174	0.008600	-0.002206	-0.053294	0.004612	0.045761	-0.007948	0.000761	-0.002085	-0.008988	0.015322	0.012692	-0.003329	-0.019651	-0.008058	0.020655	-0.013931	-0.008740	-0.008755	-0.003003	-0.008937	-0.020486	-0.017067	-0.003693	-0.012795	0.005321	0.035784	-0.028080	-0.028296	-0.021630	0.045887	-0.016972	-0.024807	0.008568	-0.000684	-0.012042	-0.047065	-0.006966	-0.018483	-0.019370	-0.012937	0.049325	0.047012	0.006320	-0.023280	-0.020928	-0.001926	0.033450	0.053262	-0.014188	0.024231	-0.022549	-0.010350	-0.009805	-0.031466	-0.020874	0.028938	-0.022338	0.030065	0.005506	-0.007459	0.047721	0.013678	-0.006152	-0.018924	-0.003052	-0.014084	-0.046133	-0.000400	0.026809	-0.008043	1.000000	0.010723	0.031373	0.031870	-0.035053	0.006738	-0.023200	-0.015953	-0.037413	-0.020490	-0.034993	-0.002015	-0.003650	-0.011034	-0.045942	0.012371	-0.028292	-0.019831	0.033922	0.018078	0.014358	0.014888	0.010447	0.013135	-0.024889	0.017603	-0.030051
500	-0.019014	0.005301	-0.005940	0.017704	0.004379	-0.035984	0.000123	-0.035410	-0.018658	-0.008783	0.041259	0.001631	-0.030778	0.011819	-0.026722	-0.039116	-0.056472	-0.004791	-0.057301	-0.070753	-0.014090	-0.004101	-0.023221	-0.019842	-0.053793	-0.062529	-0.074285	-0.020075	-0.025499	0.012284	-0.027373	-0.020503	0.032638	-0.025772	-0.012337	-0.034984	0.002560	-0.020684	0.029658	0.009084	-0.001607	-0.003953	-0.002698	0.063294	0.004318	0.040090	0.017254	-0.015998	-0.011575	0.013746	0.024898	0.010454	-0.012118	0.010346	0.005158	-0.022474	-0.000656	0.002400	-0.011950	0.001523	-0.052670	-0.043773	-0.036519	-0.024557	-0.011890	-0.042806	-0.043169	-0.033461	-0.031467	-0.044626	-0.019645	-0.001159	0.022143	0.005277	-0.001125	-0.018524	-0.000121	0.005252	-0.003261	-0.000184	-0.017518	0.063413	0.004324	0.016944	-0.014346	0.013803	0.014067	-0.011137	0.009423	0.004990	-0.053737	-0.038198	-0.036351	-0.024758	-0.035588	-0.038156	-0.028685	-0.029876	-0.041190	-0.021394	-0.007923	-0.029819	-0.030151	0.017433	0.008151	-0.003500	-0.018666	0.051685	0.002027	0.042761	-0.008817	0.025139	0.027197	0.018176	-0.030954	-0.005055	0.003269	0.012667	-0.005035	0.012399	0.007047	0.013681	-0.008619	0.024527	0.018910	0.020581	-0.046733	-0.021276	-0.034894	0.018743	-0.010422	0.016863	0.002847	-0.004412	-0.009297	-0.019664	0.038816	-0.018167	-0.042147	-0.009422	-0.042165	-0.018912	0.012986	-0.004129	-0.005213	-0.008910	0.000154	0.023681	0.006635	0.024815	0.023425	-0.022374	0.043498	0.024737	-0.045293	0.009108	0.010723	1.000000	-0.026642	0.016424	0.006515	-0.006172	-0.017814	-0.001070	0.002892	-0.016787	-0.002712	-0.009217	0.010999	0.013762	0.017284	0.021395	0.037086	0.023475	0.038268	-0.025279	-0.025335	-0.026893	-0.032501	0.026238	-0.031075	-0.002092	0.014634
510	-0.062474	0.018804	0.017035	-0.079904	-0.002970	0.060425	-0.001157	0.000543	0.059851	-0.003432	-0.002977	-0.016509	-0.243887	-0.091556	0.026975	-0.020511	-0.016241	-0.020460	-0.009878	0.012474	0.067325	0.217680	0.005899	-0.053348	0.112759	0.105690	0.099128	-0.074490	0.064497	-0.081935	-0.081136	-0.108340	-0.029342	-0.004625	0.003965	0.032002	-0.002057	0.008524	0.015902	-0.039737	0.013121	0.034583	0.062089	-0.065987	-0.002663	-0.006257	-0.000825	0.027788	-0.029362	0.000194	0.001317	0.146745	0.139328	-0.014099	-0.068395	-0.159573	-0.056362	0.014736	-0.024575	-0.020823	0.012884	0.024026	0.009859	0.010566	0.020808	-0.035366	-0.019390	0.001251	0.049736	0.071380	0.013373	0.036522	-0.044372	0.049499	0.011002	0.005620	-0.023013	-0.055755	0.030720	0.045722	0.051322	-0.068362	-0.002674	-0.002002	-0.032078	0.000194	0.152291	0.142075	-0.010802	-0.074955	0.014125	0.016386	0.007833	0.007183	0.015832	-0.017019	-0.015649	0.057149	0.069572	0.008151	0.043851	-0.014852	-0.007241	-0.052176	0.068143	0.029874	0.005689	-0.014226	0.026898	-0.016209	-0.093667	-0.035689	-0.001169	-0.000643	0.084531	-0.030155	0.015842	-0.006549	-0.005013	0.138906	0.139180	-0.010611	-0.029949	0.128726	0.128324	0.127060	-0.063321	-0.090237	-0.124255	-0.024188	-0.025517	0.011721	0.053794	-0.125478	0.023011	-0.002853	-0.091750	-0.072915	-0.072338	0.077368	0.076482	-0.016809	0.089599	0.003661	-0.088271	0.059459	-0.004872	-0.003329	-0.046958	0.019902	0.013446	0.065898	-0.020953	-0.042066	-0.005931	0.046926	0.031373	-0.026642	1.000000	0.008942	0.115756	0.002855	-0.015796	0.012276	0.002342	0.008437	0.012393	0.055554	0.113570	0.129426	0.058223	0.041541	-0.022312	0.000876	-0.016115	-0.049337	-0.051695	-0.045304	-0.064290	0.022443	0.026216	0.026100	0.131587
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581	-0.017120	0.054006	-0.007054	-0.071311	0.088167	0.042446	-0.038481	0.014444	-0.013449	-0.030599	-0.014964	0.004892	-0.005684	-0.050235	0.049381	0.055536	0.070753	-0.037160	0.066597	0.008090	0.028175	-0.024339	0.062491	0.020490	-0.025566	0.032081	0.005661	0.068390	-0.014739	0.047279	0.079040	0.084174	0.032663	-0.023746	0.021523	0.040042	0.113359	-0.014041	-0.086257	-0.005461	0.023882	-0.007996	-0.007293	-0.017651	0.088316	0.132325	-0.000877	0.032126	0.001699	0.001700	-0.006966	0.030538	0.023542	0.027488	-0.001550	0.025069	0.026616	-0.025730	0.014553	-0.018201	0.008740	-0.015554	-0.013579	-0.018222	-0.052761	-0.011346	-0.016707	-0.038164	-0.012580	-0.009575	-0.027124	-0.016115	0.048860	0.036622	0.082655	-0.024019	-0.010013	-0.010013	0.017618	-0.000647	0.013826	-0.023399	0.088322	-0.001280	0.000686	0.001825	0.033562	0.025810	0.029577	-0.008810	0.008965	-0.014621	-0.017959	-0.016671	-0.017854	-0.014780	-0.034873	-0.013715	-0.009651	-0.027568	-0.008305	-0.022426	-0.021490	0.057954	0.044492	0.090553	-0.023920	0.003536	0.082179	0.131296	0.029608	0.017497	0.000425	-0.000842	0.006998	0.001176	0.018890	-0.015555	0.021782	0.043900	0.045770	-0.000245	0.012365	0.045354	0.046341	0.048985	-0.011265	-0.010737	-0.025780	0.000346	0.018000	-0.009663	-0.001254	0.009232	-0.001237	-0.020401	-0.009976	-0.017321	-0.006029	-0.027197	-0.010881	0.000069	-0.034135	-0.036335	0.040059	-0.077548	0.023266	0.049260	0.005895	-0.018649	0.034226	0.007107	0.012716	0.046968	0.014671	0.032639	0.013135	0.026238	0.022443	0.043442	-0.010712	0.045955	-0.015159	0.062489	-0.021175	-0.026980	0.047599	0.023380	-0.007122	0.000251	-0.017285	0.003698	-0.011938	-0.017344	0.011714	-0.012384	-0.011594	-0.012419	-0.009745	1.000000	-0.000471	0.585288	-0.013601
585	0.023589	0.002273	0.015752	0.026019	-0.001616	-0.039517	0.000523	0.017745	-0.002637	-0.008558	-0.002240	-0.010149	-0.036787	0.007846	-0.000745	0.105375	0.097681	-0.020736	0.084296	-0.004932	0.010061	-0.027998	0.006072	0.071689	0.012442	0.047657	0.049965	0.072824	0.009573	-0.005903	0.066044	-0.000381	0.004822	-0.003146	-0.009127	0.005146	0.010785	-0.008152	-0.032886	0.002639	0.006032	-0.011129	-0.025242	-0.004993	-0.002017	-0.036200	0.005805	0.002338	0.000615	-0.000068	0.021361	0.057848	-0.007748	-0.021092	-0.017733	-0.051166	0.024443	0.038999	-0.006741	0.024482	0.005484	0.011361	-0.005643	0.017493	0.001461	0.022453	0.007852	0.006587	0.007178	0.003759	0.002861	0.043542	-0.011547	0.000029	0.002583	-0.005399	-0.000598	0.008659	0.014139	0.002212	-0.027947	-0.008326	-0.002015	0.005081	-0.000487	-0.000058	0.050509	-0.007382	-0.020954	-0.017163	0.005600	0.005323	-0.003708	0.010814	0.008062	0.005178	0.003995	0.008340	0.006706	0.000406	0.031904	-0.003688	-0.001741	-0.016836	0.004501	0.011482	-0.005486	-0.017850	-0.001422	-0.030134	-0.017273	-0.027024	0.000457	0.007777	-0.005335	-0.001079	0.000673	-0.010985	0.004082	0.023100	-0.009110	-0.013933	-0.019441	-0.004271	-0.007680	-0.006692	-0.022548	-0.026866	0.026093	0.004392	-0.008347	-0.006414	-0.003953	-0.001777	0.021418	0.018212	-0.023390	0.005076	-0.001499	0.023803	0.006728	-0.001669	0.036367	0.001089	0.001931	0.015514	-0.003509	-0.032172	0.023124	0.014772	0.027997	0.029287	-0.010933	-0.009952	0.057347	-0.001534	-0.024889	-0.031075	0.026216	-0.026200	-0.002231	0.008395	-0.005138	0.000488	0.003167	-0.003234	-0.012298	-0.021702	0.006251	0.004231	0.026898	0.023803	-0.010457	0.055153	0.010143	-0.017179	-0.016812	-0.017147	-0.023910	-0.000471	1.000000	-0.003800	0.005034
589	0.004174	0.044797	-0.032890	-0.080341	0.050910	0.043777	-0.036720	0.009764	-0.030761	0.004419	-0.037950	-0.016735	-0.085175	-0.060163	0.079700	0.017425	0.054831	-0.009350	0.068414	0.003419	0.080610	0.042628	0.065373	0.011885	0.049805	0.045951	0.062864	0.022853	0.025468	0.013833	0.059547	0.040782	0.003765	-0.001901	0.035732	0.034887	0.063218	-0.012075	-0.080567	-0.015311	0.010788	-0.016306	-0.007951	-0.044007	0.050909	0.071705	-0.025198	0.013957	0.052482	-0.025900	-0.020997	0.037101	0.012282	0.008285	-0.030163	0.026381	0.030697	-0.047126	0.020697	-0.025498	0.037813	0.071597	0.053303	0.051722	0.027411	0.063565	0.091914	0.064418	0.062954	0.058584	0.057852	-0.002409	0.023060	0.038228	0.060942	-0.013656	-0.031775	-0.031872	0.007797	-0.005934	0.027428	-0.045997	0.050924	-0.024268	0.064870	-0.025821	0.040391	0.015086	0.011792	-0.035133	0.032077	0.060694	0.041811	0.044236	0.043967	0.099258	0.075016	0.043262	0.053464	0.044773	0.008217	0.061612	0.064424	0.027144	0.039242	0.064612	-0.013516	-0.017308	0.052294	0.070211	0.026880	0.009326	0.028031	-0.024310	-0.046007	0.054917	-0.016578	-0.002560	0.009502	0.045654	0.034040	-0.016258	-0.014079	0.044799	0.044365	0.046530	-0.056559	-0.072155	-0.056109	0.049000	0.024707	0.047633	-0.005916	-0.020457	0.045506	0.052503	-0.018694	0.025195	0.040092	-0.026838	0.052179	0.120153	-0.021253	-0.010267	0.013663	-0.071487	-0.018679	0.034406	-0.012958	-0.010217	-0.007881	0.015704	0.026163	0.020640	0.021782	0.034176	0.017603	-0.002092	0.026100	0.049598	0.013606	0.041534	0.033088	0.049890	0.011418	-0.011418	0.057244	0.037897	0.033783	0.030325	-0.027873	0.020439	-0.025276	-0.016591	-0.010583	-0.022672	-0.020471	-0.022567	-0.024766	0.585288	-0.003800	1.000000	-0.002653
Pass/Fail	-0.025141	-0.002603	-0.000957	-0.024623	-0.013756	0.016239	-0.005969	-0.002884	0.107997	-0.073380	0.012873	-0.018297	-0.106767	-0.051763	-0.020470	0.008347	-0.009413	-0.015267	-0.015175	0.006005	-0.008948	0.155771	-0.018342	-0.019012	0.059912	0.076551	0.054817	-0.039026	0.039532	-0.052078	-0.052013	-0.002040	0.006368	-0.016896	0.026865	-0.053476	-0.043654	-0.016720	0.067789	-0.027404	0.011298	0.029721	0.047551	-0.004781	-0.013614	-0.015427	-0.005603	-0.012131	-0.022415	-0.004610	0.001564	0.077873	0.089689	-0.019509	-0.005804	-0.078648	-0.031269	0.055601	-0.009002	0.005704	0.047165	0.053376	0.055967	0.051981	0.060595	0.028805	0.013425	0.045479	0.030319	0.053645	0.035367	-0.022763	-0.001068	0.019946	0.006964	-0.006634	0.006811	-0.028253	0.026889	0.030947	0.024699	-0.005305	-0.013617	-0.006680	-0.021192	-0.004802	0.081761	0.091831	-0.015784	-0.003912	0.039663	0.034720	0.048705	0.034514	0.039585	0.006829	0.029948	0.034594	0.049724	0.030853	-0.023049	-0.002490	-0.002465	-0.004971	0.029402	0.019723	-0.006805	0.008378	-0.006379	-0.019362	-0.042632	-0.030177	0.014251	-0.005196	0.047902	-0.022858	0.013846	-0.026333	0.000275	0.109569	0.120304	0.004431	0.047553	0.111609	0.108573	0.106426	0.015519	-0.020213	-0.007446	0.000862	-0.008346	0.060587	0.036375	-0.055426	0.038237	0.059643	0.001219	0.009500	0.014789	0.028547	0.056174	-0.006763	-0.011571	-0.009226	-0.023892	-0.043752	-0.030856	0.025411	-0.011565	-0.046372	0.019338	-0.006711	0.035182	-0.003532	-0.006456	0.025188	-0.030051	0.014634	0.131587	0.054925	0.057574	0.007691	0.036722	0.013677	-0.010401	-0.005837	0.011283	0.043849	0.008555	0.049021	0.022330	0.026177	-0.043051	0.045828	-0.001656	-0.032233	-0.034713	-0.028488	-0.049633	-0.013601	0.005034	-0.002653	1.000000

	0	1	2	3	4	6	12	15	21	22	23	24	28	40	43	45	46	48	51	55	59	60	62	63	64	65	66	67	68	71	72	73	88	90	115	117	133	135	136	137	138	139	142	148	150	151	159	160	161	162	180	182	183	185	187	188	196	197	200	201	202	203	205	208	223	225	250	268	345	346	412	417	418	419	423	428	429	430	431	432	433	438	439	440	456	460	467	468	470	472	473	474	476	478	480	482	483	484	485	486	487	488	489	490	494	496	499	500	510	511	519	520	521	523	525	547	548	550	555	561	562	569	570	572	574	577	581	585	589
0	0.224309	0.849725	-0.436273	0.033555	-0.050580	-0.563790	0.762960	0.103782	0.318407	0.735468	-0.172210	0.361942	-1.522972	-0.277120	-0.452853	-0.812566	-0.852092	0.277786	1.000167	-0.862453	-0.492923	-0.703759	-0.912878	0.303390	0.238540	0.338549	-1.112014	-0.051474	0.274977	-0.641197	2.202065e-15	-2.756181e-14	-1.124999	-0.399317	0.025089	-0.030126	-0.508797	0.087736	-0.500002	-0.911233	-0.928122	-0.252239	0.039159	0.202177	-0.785516	-0.121545	0.136767	0.716844	-0.708552	-0.676486	0.585204	0.410790	-1.456170	-0.229080	-0.784050	1.020904	-0.210990	0.000692	-0.842151	-0.483037	-0.420311	-0.794544	-0.035615	0.858783	0.949805	2.111519	2.020788	2.915403	1.445095e-16	-7.409720e-17	-0.330741	-0.544682	0.712654	-0.950558	-0.736484	-0.089261	-0.087582	0.009596	0.296967	-0.579100	-0.693900	-0.299206	-0.739976	-1.658054	-0.760944	0.055677	-0.005415	0.379989	-0.519154	-1.557287	-0.654667	-0.967680	0.795451	-0.025367	1.126992	1.057855	0.443864	1.321592	-0.105725	1.896453	-0.914914	-1.197906	-1.203118	-0.174360	0.184098	-0.526473	-0.811180	-0.746780	0.236543	-0.837955	1.524217e-16	-0.124479	-0.112621	-0.083086	-0.622220	-1.725401	0.023303	-0.900504	-0.555732	0.531334	-8.200826e-14	-9.956414e-15	0.190142	-0.226018	-0.226665	-0.135520	5.175234e-16	-0.197113	-1.665950e-15
1	1.107136	-0.382910	1.017137	0.153067	-0.060045	0.198217	0.181365	0.095858	0.282478	-0.322234	0.223050	-0.462653	-0.311560	0.433389	-0.528735	-0.455157	-0.727349	1.298783	0.784225	-0.123389	-0.226438	-0.524040	-0.292963	-0.434592	-0.271798	0.070146	-0.816767	-0.050700	1.638845	-0.695974	2.202065e-15	-2.756181e-14	2.313733	-1.079102	-0.329673	0.006493	-0.910429	-0.347544	-1.076326	-0.799950	-0.114196	-0.751798	-0.266339	0.206199	-0.163470	-0.031341	-0.320415	-0.864326	-0.890126	-0.232181	-0.309186	-0.153925	-1.508133	-0.062045	-1.032759	1.833098	-0.315577	-0.047747	-1.105172	-0.805146	-0.446433	-0.860664	-0.007826	2.177719	0.101235	2.223278	1.527628	1.398051	1.445095e-16	-7.409720e-17	-0.867453	0.214656	-1.114221	0.184261	-0.484365	-0.133261	-0.157359	-0.221601	-0.552599	-0.720873	-0.025415	-0.181118	-0.862419	0.793499	-0.665262	0.844178	-0.174436	1.038832	-0.463083	-1.352250	-0.890206	-0.985241	0.719979	-0.025367	2.452496	-1.142265	0.210182	-1.018301	0.728007	-0.602924	2.221170	-0.632654	-1.203118	0.377697	-0.122839	-0.709929	-0.811180	-0.746780	2.275170	-0.837955	1.524217e-16	-0.187140	-0.112621	-0.093240	-0.624689	1.135963	-0.335951	-0.223469	-0.141962	-0.745572	-8.200826e-14	-9.956414e-15	0.256816	-0.261137	-0.265730	-0.460054	2.007880e+00	0.385059	1.156689e+00
2	-1.114158	0.799102	-0.481289	0.686213	-0.047906	-0.906210	0.633372	0.210561	0.272497	0.008033	-0.174567	-0.557914	-0.684304	-2.242749	1.418662	-0.629808	0.092052	0.246003	0.465399	3.105153	2.194423	1.559140	-0.101633	-0.378507	-0.879523	-0.401058	0.331202	-0.051647	-0.387078	-0.619387	-7.487774e-01	1.393693e+00	-2.278834	1.256492	-0.588677	-0.022382	-0.858902	-0.504244	-0.219277	-0.502556	-1.017646	-0.422569	0.477605	-0.010966	-0.616260	-0.181004	-0.326525	0.405138	-0.781040	-0.411950	-0.372639	0.607839	-0.548787	0.256713	-0.007704	0.778808	-0.229484	-0.032550	-0.100386	-0.539995	0.060919	-0.427553	0.369064	0.226882	1.081806	-0.472988	-0.096130	1.381392	4.562218e-01	8.074462e-01	-0.703292	-0.732677	-1.114221	-0.950558	-0.829909	-0.044628	-0.101012	-0.224990	0.187236	-0.639442	-0.425357	0.155507	0.007099	-0.431880	0.339049	0.237267	-0.372060	-0.881098	0.797420	0.213138	-0.355337	0.319282	-0.452152	-0.025367	0.515421	0.415586	-0.286419	-1.018301	-0.049437	1.558987	-0.631890	-0.643368	-0.097230	-0.953410	0.087593	-0.449449	-0.811180	-0.746780	4.913293	-0.096265	2.160440e+00	0.046768	-0.112621	-0.091359	1.702945	1.641698	-0.331428	-0.188183	-1.078814	-0.396160	6.253138e-01	5.144841e+00	0.257279	-0.199823	-0.188395	-0.590505	-2.744816e-01	0.029833	-1.791486e-01
3	-0.350312	-0.198875	-0.051547	-1.106948	-0.051290	0.503246	0.581223	0.535107	0.239761	-0.173190	-0.513983	-0.468515	-1.898926	0.376833	1.201850	-0.629808	0.016854	0.568516	0.533568	3.105153	2.252754	1.047336	-0.037229	-0.060581	-0.991231	-0.514988	0.322131	-0.051299	0.051274	-1.079763	7.716409e-01	-2.462267e-01	-1.041069	-1.595753	-0.782633	0.005798	-3.619030	0.157381	-0.375442	0.019322	-0.797894	-0.970606	-0.320084	0.256042	0.697634	0.038549	-0.024112	-0.348879	-0.149663	-0.273877	-0.844007	0.934653	-0.600019	-0.233256	0.030648	0.431357	-0.176553	0.333121	-0.466077	-0.376977	0.083064	-0.330209	0.044278	1.131454	0.530337	-0.518068	-0.285006	1.683033	3.613835e-02	-7.384135e-02	-0.712547	-0.131018	-0.008778	-0.950558	0.516013	-0.133629	0.127143	-0.075299	-0.246234	-0.185077	-0.107532	-0.606521	-0.759540	-0.713746	0.170529	-0.113683	-0.372060	-0.868418	1.410003	-0.796330	-0.068257	0.438283	1.026429	-0.025367	2.071671	-0.335060	-0.554443	0.427874	-0.518840	-0.694024	-0.640739	-1.410324	0.285546	1.198057	-0.122128	-0.025267	-0.811180	1.458534	1.534851	-0.837955	-6.528480e-01	-0.044627	-0.112621	-0.085908	0.307254	-6.646047	-1.046058	25.287741	3.225112	1.602627	7.932446e-01	4.223799e-01	0.002548	-0.221613	-0.232808	-0.645708	-4.386698e-01	-0.283417	-2.752459e-01
4	0.242143	0.087526	1.117387	-0.158919	-0.047492	-0.115382	0.610171	0.436445	0.226986	-0.217226	-0.131415	0.143314	-1.927846	0.355468	-0.352913	5.044537	4.610835	-0.416695	1.250334	0.343388	-1.604435	-0.013001	3.196010	1.124845	2.367169	2.395718	3.357220	-0.051464	-0.182947	-1.218682	2.202065e-15	-2.756181e-14	0.384333	0.479681	1.005673	-0.037036	-1.674469	0.018091	0.093053	0.007810	-0.716501	-0.459721	-0.849048	0.307867	0.676632	-0.049782	-0.187028	-0.474258	-0.547757	-0.512296	-1.140123	0.016691	1.618012	-0.471280	0.883699	1.333651	-0.202062	-0.411519	0.250309	-0.455540	-0.118231	0.215744	0.003463	0.679989	-0.973833	0.857771	0.724924	0.048793	1.445095e-16	-7.409720e-17	-0.467901	-0.980861	-1.114221	1.713028	0.232375	-0.072999	-0.046079	-0.157039	-0.319558	-0.440681	-0.911141	-0.861295	-0.865293	0.603609	-0.102354	0.127187	-0.099977	-0.778857	-0.013888	-1.007554	-0.997073	-0.747461	1.512905	-0.025367	1.812519	-0.527226	0.367536	1.166272	0.180080	-1.061192	1.329344	1.582088	-1.203118	0.207997	-0.139003	3.214616	0.092288	-0.746780	2.451234	-0.837955	1.524217e-16	-0.025401	-0.112621	-0.094615	-0.659557	-0.785745	1.123032	0.577065	0.411426	-0.538287	-8.200826e-14	-9.956414e-15	0.085279	-0.227409	-0.222385	-0.454486	5.175234e-16	26.913347	-2.752459e-01

	0	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	74
0	-1.163453	-2.396733	0.163554	0.431128	-0.291643	-1.054599	0.911534	-0.054441	0.196146	0.252531	-0.328450	-0.676090	0.127875	-1.562120	-2.102059	-1.517571	0.521249	-0.886171	-1.267300	-0.278813	-1.618662	0.917258	0.955281	-1.286690	-0.027092	-0.697406	-0.222995	1.404850	-1.150182	-0.037549	-0.356038	0.321769	0.697939	1.714255	0.493094	-0.353475	-0.703971	-0.181684	0.772531	0.318199	0.034142	1.011521	-1.101945	-1.726825	0.896541	0.272611	-0.359435	-0.035249	0.834872	2.013943	-0.701044	-0.102662	0.697089	0.352842	0.038590	0.264686	0.897474	0.713379	-0.197127	-0.824293	-1.100915	-1.135701	0.912250	0.343964	1.176808	0.868350	-0.104809	1.465931	0.562727	-0.485682	-1.254538	-1.329805	-1.137506	0.287792	-1.246909
1	-1.545191	-1.744298	0.432179	0.378855	-0.183718	-1.421468	0.841136	-0.487645	-0.078498	0.349606	-0.895909	0.660900	-1.229965	-0.988639	-3.503881	-0.486649	1.745170	-2.271156	0.148130	-1.292155	0.297530	0.246258	0.353732	1.303990	0.279255	-2.386071	1.365830	-0.865122	-1.248437	-0.876139	-1.141848	-0.665323	0.112224	1.797491	0.657309	-0.569923	1.200862	1.056545	-0.061525	-0.629477	-0.036881	-0.339568	1.372847	-0.260934	-0.338792	-0.183522	0.430305	-0.640051	1.164547	-0.804323	-0.245761	-0.682826	-0.819494	0.608515	0.447998	0.196643	0.343539	-0.894908	-0.397173	1.070460	0.864844	0.592388	0.841974	0.905768	0.854948	2.109583	-0.812823	0.767246	2.532387	0.044418	0.367491	-0.114228	-0.611888	1.142240	-1.019391
2	0.860045	-1.651834	-1.221206	2.390579	0.593729	-1.735366	-0.719669	1.419358	-1.838536	-1.390172	-0.428844	-0.834210	-0.439010	0.714487	-1.261344	0.879023	1.214892	-0.574733	-0.032666	-1.922532	-0.470613	0.672419	-1.926182	1.059461	-0.143582	2.783651	-1.257360	1.053837	0.457986	1.791640	2.586380	0.082402	0.257790	1.653516	1.819283	1.734753	-1.572411	-0.824989	0.964750	-0.173618	-1.692548	0.054716	-0.366977	-1.029313	1.052382	-0.603086	0.909156	-2.641156	-0.267543	1.334477	-1.591412	0.111139	-1.018543	-1.131141	-0.886501	-0.400448	0.867028	-0.120111	-3.325047	-0.810216	0.622516	1.366869	-1.204378	0.227972	-1.102934	1.381201	-0.213805	1.223106	1.696287	0.430952	0.894538	0.066978	-0.277683	0.725075	-0.608610
3	0.882356	-2.788030	-1.610578	4.655940	-2.106891	-3.046918	-1.481106	2.204595	-1.013725	1.142482	-0.711539	0.359093	-0.521430	3.022199	-4.156036	-0.139373	2.416940	1.022970	0.119292	-0.963635	1.609175	0.911552	-1.536609	2.382004	2.289904	0.128736	-0.985622	2.985585	7.071971	4.800494	-3.579417	-4.415538	-3.338633	2.584572	2.992760	-3.359439	7.321620	-1.604284	0.382782	6.859341	-8.374694	-2.556433	-2.034077	0.718443	3.790040	6.091768	0.899226	7.192738	-0.658418	-2.436175	5.199513	4.092708	1.319333	-3.052296	-0.682244	3.110848	-0.042547	5.313440	-3.446089	2.394313	0.649692	-0.301269	3.792983	-3.045137	1.388543	-2.798867	-4.526912	3.008675	-3.131251	3.054224	-2.417240	0.952617	3.307447	-0.547730	0.930823
4	1.645215	0.586966	-2.916893	6.848357	-0.287891	3.910116	4.339091	-4.159801	2.497264	-0.248956	-0.483843	0.720155	2.048752	3.960142	-0.757799	2.215194	1.605159	-3.820181	-3.574692	0.189568	-0.753329	0.762581	0.251675	4.069546	-1.438865	-0.826142	0.519724	-3.458778	-2.259994	-4.179893	-0.028555	-1.880137	-2.119496	6.756367	-1.145346	6.882802	7.321450	0.391154	4.918356	7.431908	-1.824904	7.144568	-0.079447	10.735411	-2.646822	-2.554352	2.262293	2.369519	-2.962687	2.446161	-7.411788	0.536720	2.274107	1.012001	2.876127	-5.577185	-3.735778	-1.741377	-1.905116	3.394340	1.138413	-2.911084	-0.881005	1.078923	4.077711	2.378935	2.317550	-3.084827	-1.722713	-0.184079	-0.596113	1.427634	1.722657	0.156708	-0.395822

	93	112	16	73	106	42	51	45	3	109	126	102	1	115	91	7	49	36	128	82	48	116	88	38	72	39	70	26	33	122	95	5	54	79	98	69	63	96	12	89	75	14	11	87	66	99	44	107	10	59	110	21	60	105	47	100	83	37	31	53	15	35	28	50	101	41	4	34	67	9	19	68	94	55	127	71	86	30	64	117	90
0	-0.0001	NaN	10.0433	NaN	0.0000	70	218.3174	130.3691	1411.1265	NaN	3.353	0.1350	2564.00	748.6115	-0.3274	0.1242	1	49.3830	3.188	-0.0044	141.2282	0.9908	1747.6049	86.9555	NaN	117.5132	608.1700	1.7730	9.5126	2.639	0.0003	100	4.841	-0.0083	0.3974	1	16.1445	-0.2786	202.4396	0.1841	0.0126	7.9558	0.9455	0.9690	693.7724	-0.0251	10.1841	-0.2468	-0.0034	-1.7264	NaN	-5419.00	350.9264	0.0056	1.3072	0.0002	7.2163	66.3141	3.5191	4.592	414.8710	64.2588	64.2333	624.3145	0.0002	4.515	1.3602	50.6170	0.9226	0.0162	12.5190	148.6009	0.0001	2834	0.4098	84.0793	2.3895	0.1632	21.7264	58.4306	8671.9301
1	0.0000	NaN	9.2599	NaN	0.0000	70	205.1695	133.1727	1463.6606	NaN	2.771	-0.0752	2465.14	731.2517	0.1455	0.1247	1	49.3404	3.272	0.0017	145.8445	0.9902	1931.6464	87.5241	NaN	118.1188	620.3582	2.0143	9.7997	2.541	0.0001	100	4.842	-0.0358	-0.9353	1	10.9036	0.5854	200.5470	0.1874	-0.0039	10.1548	0.9627	0.9894	697.1964	-0.0158	10.0373	0.0772	-0.0148	0.8073	NaN	-5441.50	352.0073	0.0015	1.2887	-0.0004	6.8043	64.9193	3.4171	4.590	414.7347	64.2828	68.4222	631.2618	-0.0004	2.773	0.8294	50.6596	1.1598	-0.0005	12.4608	154.3709	-0.0005	2853	0.4138	82.3494	2.3754	0.2102	19.1927	58.6680	8407.0299
2	-0.0013	0.4684	9.3144	485.2665	0.0013	70	185.7574	131.8027	1698.0172	NaN	3.094	0.0134	2559.94	718.5777	0.0553	0.1241	1	49.8470	3.272	0.0287	141.0845	0.9899	1685.8514	84.7327	140.6972	118.6128	625.9636	2.0295	8.6590	2.882	0.0002	100	4.748	-0.0054	-0.1427	1	11.3019	-0.1343	202.0179	0.1497	-0.0078	9.5157	0.9615	0.9880	710.5095	0.1218	9.8783	-0.0301	0.0013	23.8245	NaN	-5447.75	364.5364	-0.0006	1.2992	0.0006	7.1041	65.8389	3.5986	4.486	416.7075	64.1114	67.1333	637.2655	-0.0001	5.434	1.5102	50.1530	0.8694	0.0041	12.5404	145.8000	0.0000	2936	0.4777	84.7681	2.4532	0.1734	16.1755	58.4808	9317.1698
3	-0.0021	0.4647	9.6924	464.9735	0.0021	70	189.9079	131.8027	909.7926	NaN	2.480	-0.0699	2479.90	709.0867	0.0697	0.1217	1	49.4900	3.119	0.0277	142.5427	0.9906	1752.0968	86.6867	160.3210	117.0442	625.2945	2.0038	8.6789	3.132	0.0002	100	4.748	0.0676	0.0177	1	13.5597	0.0411	201.8482	0.1958	-0.0555	9.6052	0.9629	0.9904	710.4043	-0.0195	9.9305	-0.0483	-0.0033	24.3791	NaN	-5468.25	361.4582	0.0003	1.3027	-0.0002	7.5925	65.1951	3.3813	4.486	422.2894	64.1125	62.9333	637.3727	0.0000	1.279	1.3204	50.5100	0.9761	-0.0124	12.4782	147.6545	-0.0001	2936	0.5176	70.2289	2.4004	0.2071	15.6209	58.6635	8205.7000
4	-0.0001	NaN	10.3387	NaN	0.0000	70	233.5491	176.3136	1326.5200	NaN	3.027	0.0696	2502.87	796.5950	0.0448	0.1235	1	49.7520	3.299	-0.0048	138.0882	0.9908	1828.3846	86.1468	NaN	121.4364	645.7636	1.9912	8.7677	3.148	-0.0001	100	4.894	0.0034	-0.6704	1	21.9782	0.2189	201.9424	0.1829	-0.0534	10.5661	0.9569	0.9902	745.6025	-0.0167	10.4091	-0.0799	-0.0072	-12.2945	NaN	-5476.25	355.0809	0.0078	1.0341	0.0004	7.5017	66.1542	3.2728	4.624	420.5925	64.1511	62.8333	667.7418	-0.0003	2.209	1.5334	50.2480	0.9256	-0.0031	12.4735	146.6636	-0.0001	2865	0.5328	65.8417	2.4530	0.2696	32.2945	58.3858	9014.4600

	count	mean	std	min	25%	50%	75%	max
93	18.0	-0.000883	0.001360	-0.0033	-0.001800	-0.00085	-0.000100	0.0018
112	11.0	0.464391	0.004575	0.4564	0.461150	0.46470	0.466750	0.4727
16	18.0	9.738322	0.387699	9.1999	9.436200	9.69850	10.032225	10.4109
73	8.0	469.437937	6.680888	464.2860	465.804650	468.06705	469.301075	485.2665
97	18.0	0.000000	0.000000	0.0000	0.000000	0.00000	0.000000	0.0000
106	18.0	0.000933	0.001272	-0.0019	0.000000	0.00085	0.001775	0.0033
42	18.0	70.000000	0.000000	70.0000	70.000000	70.00000	70.000000	70.0000
51	18.0	190.206028	45.206035	112.7524	152.852575	198.06390	217.976500	255.6310
45	18.0	139.672172	11.478393	120.2818	132.552300	139.79000	142.599300	176.3136
3	18.0	1200.046806	255.818978	877.6266	1004.469200	1152.30130	1389.974875	1698.0172
109	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
126	18.0	2.800000	0.214799	2.4800	2.645000	2.77050	2.854000	3.3530
102	18.0	0.020028	0.061977	-0.0752	-0.010250	0.00910	0.049775	0.1638
1	18.0	2526.248889	70.409195	2428.3700	2479.525000	2523.53000	2560.637500	2690.1500
115	18.0	742.226317	42.219709	636.9547	726.798325	748.32585	767.609250	806.2734
91	18.0	-0.027594	0.115585	-0.3274	-0.091900	-0.00500	0.044100	0.1455
52	18.0	0.000000	0.000000	0.0000	0.000000	0.00000	0.000000	0.0000
7	18.0	0.122056	0.002388	0.1185	0.119950	0.12230	0.124075	0.1257
49	18.0	1.000000	0.000000	1.0000	1.000000	1.00000	1.000000	1.0000
36	18.0	49.628617	0.186347	49.3404	49.495175	49.55155	49.810100	49.9405
128	18.0	3.280167	0.101508	3.1190	3.207000	3.27200	3.362000	3.4420
82	18.0	0.011356	0.031623	-0.1003	0.001025	0.02405	0.028775	0.0356
48	18.0	139.389994	3.023645	136.0836	137.266150	138.15270	140.843150	146.4855
116	18.0	0.988294	0.006531	0.9692	0.990200	0.99055	0.990775	0.9911
88	18.0	1773.365767	75.059770	1632.3120	1720.881125	1764.71245	1827.816225	1931.6464
38	18.0	86.740378	0.946658	84.7327	86.447825	86.74930	87.009350	88.3312
72	8.0	157.012075	9.026895	140.6972	151.653225	158.88595	161.117125	170.5815
39	18.0	118.255900	1.757665	116.5360	117.137900	117.63820	118.523750	122.0886
70	18.0	627.287767	10.565195	608.1700	619.602050	626.75225	635.602050	645.7636
26	18.0	1.667467	0.468792	0.4390	1.770000	1.78010	2.000650	2.0295
33	18.0	9.064589	0.420413	8.5762	8.691400	8.96300	9.434625	9.7997
122	18.0	2.806500	0.362844	2.2700	2.549500	2.77150	3.144000	3.2630
95	18.0	0.000128	0.000102	-0.0001	0.000100	0.00015	0.000200	0.0003
5	18.0	100.000000	0.000000	100.0000	100.000000	100.00000	100.000000	100.0000
114	18.0	0.000000	0.000000	0.0000	0.000000	0.00000	0.000000	0.0000
54	18.0	4.854056	0.068076	4.7480	4.814250	4.86800	4.915000	4.9310
79	18.0	0.005017	0.031852	-0.0423	-0.007575	0.00000	0.008650	0.0676
98	18.0	-0.111244	0.406094	-0.9353	-0.216500	-0.10710	0.097575	0.6241
69	18.0	1.000000	0.000000	1.0000	1.000000	1.00000	1.000000	1.0000
63	18.0	11.059100	4.154847	5.5936	8.108725	10.56545	12.715000	21.9782
96	18.0	0.036689	0.202023	-0.3062	-0.044325	0.02935	0.139050	0.5854
12	18.0	201.648722	6.842171	192.9787	200.490750	201.98015	202.344475	226.0086
89	18.0	0.325056	0.402583	0.1497	0.172900	0.19045	0.203150	1.4727
75	18.0	-0.010433	0.020641	-0.0555	-0.017800	-0.00970	0.001650	0.0227
14	18.0	10.437922	1.684240	7.9558	9.210225	9.88440	12.099525	13.6872
11	18.0	0.955267	0.008500	0.9433	0.946150	0.95865	0.961575	0.9699
87	18.0	0.981283	0.010189	0.9642	0.969125	0.98815	0.989300	0.9906
66	18.0	714.938828	14.263917	693.7724	704.799750	712.57435	727.037275	745.6025
99	18.0	0.008739	0.045102	-0.0419	-0.018800	-0.00510	0.020450	0.1218
44	18.0	9.959256	0.202864	9.7235	9.793700	9.91795	10.055825	10.4091
107	18.0	-0.038622	0.112880	-0.2601	-0.072000	-0.02260	0.027925	0.1359
10	18.0	-0.001594	0.008882	-0.0154	-0.005875	-0.00290	0.001300	0.0216
59	18.0	13.809850	14.968587	-12.2945	-0.692700	21.52180	26.505225	29.1309
110	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN
21	18.0	-5018.138889	1354.173579	-6154.0000	-5721.062500	-5458.00000	-5367.000000	-1431.5000
60	18.0	358.418494	5.352772	350.9264	352.345875	360.18955	362.942075	365.0464
105	18.0	0.000867	0.003325	-0.0052	-0.000750	0.00025	0.002650	0.0078
47	18.0	1.141200	0.153443	0.8974	0.976875	1.19310	1.260575	1.3072
100	18.0	0.000061	0.000366	-0.0004	-0.000200	0.00005	0.000300	0.0008
83	18.0	7.308333	0.562315	6.2166	6.879250	7.49290	7.582850	8.4126
37	18.0	65.855422	0.587223	64.9193	65.195100	66.10835	66.327675	66.5050
31	18.0	3.489156	0.084033	3.2728	3.459175	3.50480	3.521450	3.6651
53	18.0	4.590944	0.056058	4.4860	4.563750	4.60800	4.636000	4.6470
15	18.0	417.363083	5.290284	408.4017	414.365625	416.29635	420.562525	428.9826
35	18.0	64.149456	0.151875	63.8294	64.096625	64.13180	64.240675	64.4021
28	18.0	64.335794	2.741885	60.2444	62.575000	63.36670	67.108300	68.7111
50	18.0	637.241556	10.886987	621.4300	630.275450	637.50815	640.137250	667.7418
101	18.0	-0.000050	0.000195	-0.0004	-0.000100	-0.00005	0.000075	0.0003
41	18.0	3.227389	1.281761	1.2790	2.324500	3.12050	3.951500	5.4340
4	18.0	1.208883	0.284415	0.7884	0.871025	1.32040	1.395000	1.5334
34	18.0	50.371394	0.186354	50.0595	50.189900	50.44850	50.504825	50.6596
67	18.0	0.959961	0.078151	0.8692	0.911575	0.93570	0.992575	1.1598
9	18.0	0.003572	0.017393	-0.0270	-0.010075	0.01275	0.016125	0.0250
19	18.0	12.388433	0.421946	10.7029	12.461150	12.47610	12.524475	12.5404
68	18.0	147.609500	3.522391	142.5155	144.621175	146.90225	150.183900	154.3709
94	18.0	-0.000067	0.000146	-0.0005	-0.000100	-0.00005	0.000000	0.0002
55	18.0	2875.166667	31.267818	2834.0000	2856.000000	2865.00000	2884.000000	2936.0000
127	18.0	0.451594	0.073217	0.3273	0.413800	0.46220	0.515800	0.5328
71	18.0	82.903950	17.276064	57.0841	70.264350	81.24785	90.316525	112.0904
86	18.0	2.402406	0.050233	2.2944	2.367525	2.40005	2.453150	2.4704
30	18.0	0.196739	0.030673	0.1394	0.176150	0.19840	0.209675	0.2696
64	18.0	18.412372	6.017118	10.8691	13.494775	17.63320	21.592975	32.2945
117	18.0	58.569439	0.720623	56.5635	58.443150	58.65975	58.811875	59.5776
90	18.0	8714.982761	428.395594	7869.7000	8502.547475	8730.71510	9079.080000	9317.1698

	Time	0	1	2	3	4	5	6	7	8	9	10	11	12	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	53	54	55	56	57	58	59	60	61	62	63	64	65	66	67	68	69	70	71	72	73	75	76	77	78	79	80	81	82	83	84	85	86	87	88	89	90	91	92	93	94	95	96	98	99	100	101	102	103	104	105	106	107	108	109	110	111	112	113	115	116	117	118	119	120	121	122	123	124	125	126	127	128	129	130	131	132	133	134	135	136	137	138	139	140	142	143	144	145	146	147	148	150	151	152	153	154	155	156	157	158	159	160	161	162	163	164	165	166	167	168	169	170	171	172	173	174	175	176	177	180	181	182	183	184	185	187	188	195	196	197	198	199	200	201	202	203	204	205	207	208	210	211	212	213	214	215	216	217	218	219	220	221	222	223	224	225	227	228	238	239	244	245	246	247	248	250	251	252	253	254	255	267	268	269	270	271	272	273	274	275	277	278	279	280	281	282	283	285	286	287	288	289	290	291	292	293	294	295	296	297	298	299	300	301	302	303	304	305	306	307	308	309	310	311	312	316	317	318	319	320	321	323	324	331	332	333	334	335	336	337	338	339	340	341	343	344	345	346	348	349	350	351	352	353	354	355	356	357	358	359	360	361	362	363	365	366	367	368	376	377	382	383	384	385	386	388	389	390	391	392	393	405	406	407	408	409	410	411	412	413	415	416	417	418	419	420	421	423	424	425	426	427	428	429	430	431	432	433	434	435	436	437	438	439	440	441	442	443	444	445	446	447	448	452	453	454	455	456	457	459	460	467	468	469	470	471	472	473	474	475	476	477	479	480	482	483	484	485	486	487	488	489	490	491	492	493	494	495	496	497	499	500	510	511	516	517	518	519	520	522	523	524	525	526	527	539	540	541	542	543	544	545	546	547	548	549	550	551	552	553	554	555	556	557	558	559	560	561	562	563	564	565	566	567	568	569	570	571	572	573	574	575	576	577	578	579	580	581	582	583	584	585	586	587	588	589	Pass/Fail
0	2008-07-19 11:55:00	3030.93	2564.00	2187.7333	1411.1265	1.3602	100.0	97.6133	0.1242	1.5005	0.0162	-0.0034	0.9455	202.4396	7.9558	414.8710	10.0433	0.9680	192.3963	12.5190	1.4026	-5419.00	2916.50	-4043.75	751.00	0.8955	1.7730	3.0490	64.2333	2.0222	0.1632	3.5191	83.3971	9.5126	50.6170	64.2588	49.3830	66.3141	86.9555	117.5132	61.29	4.515	70.0	352.7173	10.1841	130.3691	723.3092	1.3072	141.2282	1.0	624.3145	218.3174	4.592	4.841	2834.0	0.9317	0.9484	4.7057	-1.7264	350.9264	10.6231	108.6427	16.1445	21.7264	29.5367	693.7724	0.9226	148.6009	1.0	608.1700	84.0793	NaN	NaN	0.0126	-0.0206	0.0141	-0.0307	-0.0083	-0.0026	-0.0567	-0.0044	7.2163	0.1320	NaN	2.3895	0.9690	1747.6049	0.1841	8671.9301	-0.3274	-0.0055	-0.0001	0.0001	0.0003	-0.2786	0.3974	-0.0251	0.0002	0.0002	0.1350	-0.0042	0.0003	0.0056	0.0000	-0.2468	0.3196	NaN	NaN	NaN	NaN	0.9460	748.6115	0.9908	58.4306	0.6002	0.9804	6.3788	15.88	2.639	15.94	15.93	0.8656	3.353	0.4098	3.188	-0.0473	0.7243	0.9960	2.2967	1000.7263	39.2373	123.0	111.3	75.2	46.2000	350.6710	0.3948	6.78	0.0034	0.0898	0.0850	0.0358	0.0328	12.2566	4.271	10.284	0.4734	0.0167	11.8901	0.41	0.0506	NaN	NaN	1017.0	967.0	1066.0	368.0	0.090	0.048	0.095	2.0	0.9	0.069	0.046	0.7250	0.1139	0.3183	0.5888	0.3184	0.9499	0.3979	0.160	20.95	0.333	12.49	16.713	0.0803	5.72	11.19	65.363	0.292	5.38	20.10	0.296	10.62	10.30	5.38	4.040	16.230	0.2951	8.64	10.30	97.314	0.0772	0.0599	0.0700	0.0547	0.0704	0.0520	0.0301	0.1135	3.4789	0.0010	NaN	0.0707	0.0211	175.2173	0.0315	1940.3994	0.0744	0.0546	0.0027	0.0040	NaN	NaN	NaN	NaN	0.0188	219.9453	0.0011	2.8374	0.0189	0.0050	0.4269	0.0472	40.855	4.5152	30.9815	33.9606	22.9057	15.9525	110.2144	0.1310	2.5883	0.0010	0.0319	0.0197	0.0120	0.0109	3.9321	1.5123	3.5811	0.1337	0.0055	3.8447	0.1077	0.0167	NaN	NaN	418.1363	398.3185	496.1582	158.3330	0.0373	0.0202	0.0462	0.6083	0.3032	0.0200	0.0174	0.2827	0.0434	0.1342	0.2419	0.1343	0.3670	0.1431	0.0610	6.2698	0.1181	3.8208	5.3737	0.0254	1.6252	3.2461	18.0118	0.0752	1.5989	6.5893	0.0913	3.0911	8.4654	1.5989	1.2293	5.3406	0.0867	2.8551	2.9971	31.8843	NaN	NaN	0.0215	0.0274	0.0315	0.0238	0.0206	0.0238	0.0144	0.0491	1.2708	0.0004	NaN	0.0229	0.0065	55.2039	0.0105	560.2658	0.0170	0.0148	0.0124	0.0114	0.0010	0.0013	NaN	NaN	NaN	NaN	0.0055	61.5932	0.0003	0.9967	0.0082	0.0017	0.1437	0.0151	14.2396	1.4392	5.6188	3.6721	2.9329	2.1118	24.8504	29.0271	6.9458	2.7380	5.9846	525.0965	0.0000	3.4641	6.0544	53.6840	2.4788	4.7141	1.7275	6.1800	3.2750	3.6084	18.7673	33.1562	26.3617	49.0013	10.0503	2.7073	3.1158	3.1136	44.5055	42.2737	1.3071	0.8693	1.1975	0.6288	0.9163	0.6448	1.4324	0.4576	0.1362	5.9396	3.2698	9.5805	2.3106	6.1463	4.0502	1.7924	29.9394	6.2052	311.6377	5.7277	2.7864	9.7752	63.7987	24.7625	13.6778	2.3394	31.9893	5.8142	1.6936	115.7408	613.3069	291.4842	494.6996	178.1759	843.1138	0.0000	53.1098	0.0000	48.2091	0.7578	NaN	2.9570	2.1739	10.0261	17.1202	22.3756	0.0000	0.0000	64.6707	0.0000	NaN	NaN	NaN	NaN	1.9864	29.3804	0.1094	4.8560	3.1406	0.5064	6.6926	2.0570	4.0825	11.5074	0.1096	0.0078	0.0026	7.116	1.0616	395.570	75.752	0.4234	12.93	0.78	0.1827	5.7349	0.3363	39.8842	3.2687	1.0297	1.0344	0.4385	0.1039	42.3877	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	533.8500	2.1113	8.95	0.3157	3.0624	0.1026	1.6765	14.9509	NaN	NaN	NaN	NaN	0.5005	0.0118	0.0035	2.3630	NaN	NaN	NaN	NaN	-1
1	2008-07-19 12:32:00	3095.78	2465.14	2230.4222	1463.6606	0.8294	100.0	102.3433	0.1247	1.4966	-0.0005	-0.0148	0.9627	200.5470	10.1548	414.7347	9.2599	0.9701	191.2872	12.4608	1.3825	-5441.50	2604.25	-3498.75	-1640.25	1.2973	2.0143	7.3900	68.4222	2.2667	0.2102	3.4171	84.9052	9.7997	50.6596	64.2828	49.3404	64.9193	87.5241	118.1188	78.25	2.773	70.0	352.2445	10.0373	133.1727	724.8264	1.2887	145.8445	1.0	631.2618	205.1695	4.590	4.842	2853.0	0.9324	0.9479	4.6820	0.8073	352.0073	10.3092	113.9800	10.9036	19.1927	27.6301	697.1964	1.1598	154.3709	1.0	620.3582	82.3494	NaN	NaN	-0.0039	-0.0198	0.0004	-0.0440	-0.0358	-0.0120	-0.0377	0.0017	6.8043	0.1358	NaN	2.3754	0.9894	1931.6464	0.1874	8407.0299	0.1455	-0.0015	0.0000	-0.0005	0.0001	0.5854	-0.9353	-0.0158	-0.0004	-0.0004	-0.0752	-0.0045	0.0002	0.0015	0.0000	0.0772	-0.0903	NaN	NaN	NaN	NaN	0.9425	731.2517	0.9902	58.6680	0.5958	0.9731	6.5061	15.88	2.541	15.91	15.88	0.8703	2.771	0.4138	3.272	-0.0946	0.8122	0.9985	2.2932	998.1081	37.9213	98.0	80.3	81.0	56.2000	219.7679	0.2301	5.70	0.0049	0.1356	0.0600	0.0547	0.0204	12.3319	6.285	13.077	0.5666	0.0144	11.8428	0.35	0.0437	NaN	NaN	568.0	59.0	297.0	3277.0	0.112	0.115	0.124	2.2	1.1	0.079	0.561	1.0498	0.1917	0.4115	0.6582	0.4115	1.0181	0.2315	0.325	17.99	0.439	10.14	16.358	0.0892	6.92	9.05	82.986	0.222	3.74	19.59	0.316	11.65	8.02	3.74	3.659	15.078	0.3580	8.96	8.02	134.250	0.0566	0.0488	0.1651	0.1578	0.0468	0.0987	0.0734	0.0747	3.9578	0.0050	NaN	0.0761	0.0014	128.4285	0.0238	1988.0000	0.0203	0.0236	0.0064	0.0036	NaN	NaN	NaN	NaN	0.0154	193.0287	0.0007	3.8999	0.0187	0.0086	0.5749	0.0411	29.743	3.6327	29.0598	28.9862	22.3163	17.4008	83.5542	0.0767	1.8459	0.0012	0.0440	0.0171	0.0154	0.0069	3.9011	2.1016	3.9483	0.1662	0.0049	3.7836	0.1000	0.0139	NaN	NaN	233.9865	26.5879	139.2082	1529.7622	0.0502	0.0561	0.0591	0.8151	0.3464	0.0291	0.1822	0.3814	0.0715	0.1667	0.2630	0.1667	0.3752	0.0856	0.1214	5.6522	0.1417	2.9939	5.2445	0.0264	1.8045	2.7661	23.6230	0.0778	1.1506	5.9247	0.0878	3.3604	7.7421	1.1506	1.1265	5.0108	0.1013	2.4278	2.4890	41.7080	NaN	NaN	0.0142	0.0230	0.0768	0.0729	0.0143	0.0513	0.0399	0.0365	1.2474	0.0017	NaN	0.0248	0.0005	46.3453	0.0069	677.1873	0.0053	0.0059	0.0081	0.0033	0.0022	0.0013	NaN	NaN	NaN	NaN	0.0049	65.0999	0.0002	1.1655	0.0068	0.0027	0.1921	0.0120	10.5837	1.0323	4.3465	2.5939	3.2858	2.5197	15.0150	27.7464	5.5695	3.9300	9.0604	0.0000	368.9713	2.1196	6.1491	61.8918	3.1531	6.1188	1.4857	6.1911	2.8088	3.1595	10.4383	2.2655	8.4887	199.7866	8.6336	5.7093	1.6779	3.2153	48.5294	37.5793	16.4174	1.2364	1.9562	0.8123	1.0239	0.8340	1.5683	0.2645	0.2751	5.1072	4.3737	7.6142	2.2568	6.9233	4.7448	1.4336	40.4475	4.7415	463.2883	5.5652	3.0652	10.2211	73.5536	19.4865	13.2430	2.1627	30.8643	5.8042	1.2928	163.0249	0.0000	246.7762	0.0000	359.0444	130.6350	820.7900	194.4371	0.0000	58.1666	3.6822	NaN	3.2029	0.1441	6.6487	12.6788	23.6469	0.0000	0.0000	141.4365	0.0000	NaN	NaN	NaN	NaN	1.6292	26.3970	0.0673	6.6475	3.1310	0.8832	8.8370	1.7910	2.9799	9.5796	0.1096	0.0078	0.0026	7.116	1.3526	408.798	74.640	0.7193	16.00	1.33	0.2829	7.1196	0.4989	53.1836	3.9139	1.7819	0.9634	0.1745	0.0375	18.1087	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	535.0164	2.4335	5.92	0.2653	2.0111	0.0772	1.1065	10.9003	0.0096	0.0201	0.0060	208.2045	0.5019	0.0223	0.0055	4.4447	0.0096	0.0201	0.0060	208.2045	-1
2	2008-07-19 13:17:00	2932.61	2559.94	2186.4111	1698.0172	1.5102	100.0	95.4878	0.1241	1.4436	0.0041	0.0013	0.9615	202.0179	9.5157	416.7075	9.3144	0.9674	192.7035	12.5404	1.4123	-5447.75	2701.75	-4047.00	-1916.50	1.3122	2.0295	7.5788	67.1333	2.3333	0.1734	3.5986	84.7569	8.6590	50.1530	64.1114	49.8470	65.8389	84.7327	118.6128	14.37	5.434	70.0	364.3782	9.8783	131.8027	734.7924	1.2992	141.0845	1.0	637.2655	185.7574	4.486	4.748	2936.0	0.9139	0.9447	4.5873	23.8245	364.5364	10.1685	115.6273	11.3019	16.1755	24.2829	710.5095	0.8694	145.8000	1.0	625.9636	84.7681	140.6972	485.2665	-0.0078	-0.0326	-0.0052	0.0213	-0.0054	-0.1134	-0.0182	0.0287	7.1041	0.1362	NaN	2.4532	0.9880	1685.8514	0.1497	9317.1698	0.0553	0.0006	-0.0013	0.0000	0.0002	-0.1343	-0.1427	0.1218	0.0006	-0.0001	0.0134	-0.0026	-0.0016	-0.0006	0.0013	-0.0301	-0.0728	NaN	NaN	NaN	0.4684	0.9231	718.5777	0.9899	58.4808	0.6015	0.9772	6.4527	15.90	2.882	15.94	15.95	0.8798	3.094	0.4777	3.272	-0.1892	0.8194	0.9978	2.2592	998.4440	42.0579	89.0	126.4	96.5	45.1001	306.0380	0.3263	8.33	0.0038	0.0754	0.0483	0.0619	0.0221	8.2660	4.819	8.443	0.4909	0.0177	8.2054	0.47	0.0497	NaN	NaN	562.0	788.0	759.0	2100.0	0.187	0.117	0.068	2.1	1.4	0.123	0.319	1.0824	0.0369	0.3141	0.5753	0.3141	0.9677	0.2706	0.326	17.78	0.745	13.31	22.912	0.1959	9.21	17.87	60.110	0.139	5.09	19.75	0.949	9.71	16.73	5.09	11.059	22.624	0.1164	13.30	16.73	79.618	0.0339	0.0494	0.0696	0.0406	0.0401	0.0840	0.0349	0.0718	2.4266	0.0014	NaN	0.0963	0.0152	182.4956	0.0284	839.6006	0.0192	0.0170	0.0062	0.0040	NaN	NaN	NaN	0.1729	0.0273	104.4042	0.0007	4.1446	0.0733	0.0063	0.4166	0.0487	29.621	3.9133	23.5510	41.3837	32.6256	15.7716	97.3868	0.1117	2.5274	0.0012	0.0249	0.0152	0.0157	0.0075	2.8705	1.5306	2.5493	0.1479	0.0059	2.8046	0.1185	0.0167	NaN	NaN	251.4536	329.6406	325.0672	902.4576	0.0800	0.0583	0.0326	0.6964	0.4031	0.0416	0.1041	0.3846	0.0151	0.1288	0.2268	0.1288	0.3677	0.1175	0.1261	5.7247	0.2682	3.8541	6.1797	0.0546	2.5680	4.6067	16.0104	0.0243	1.5481	5.9453	0.2777	3.1600	8.9855	1.5481	2.9844	6.2277	0.0353	3.7663	5.6983	24.7959	13.5664	15.4488	0.0105	0.0208	0.0327	0.0171	0.0116	0.0428	0.0154	0.0383	0.7786	0.0005	NaN	0.0302	0.0046	58.0575	0.0092	283.6616	0.0054	0.0043	0.0030	0.0037	0.0021	0.0015	NaN	NaN	NaN	0.0221	0.0100	28.7334	0.0003	1.2356	0.0190	0.0020	0.1375	0.0190	11.4871	1.1798	4.0782	4.3102	3.7696	2.0627	18.0233	21.6062	8.7236	3.0609	5.2231	0.0000	0.0000	2.2943	4.0917	50.6425	2.0261	5.2707	1.8268	4.2581	3.7479	3.5220	10.3162	29.1663	18.7546	109.5747	14.2503	5.7650	0.8972	3.1281	60.0000	70.9161	8.8647	1.2771	0.4264	0.6263	0.8973	0.6301	1.4698	0.3194	0.2748	4.8795	7.5418	10.0984	3.1182	15.0790	6.5280	2.8042	32.3594	3.0301	21.3645	5.4178	9.3327	8.3977	148.0287	31.4674	45.5423	3.1842	13.3923	9.1221	2.6727	93.9245	434.2674	151.7665	0.0000	190.3869	746.9150	74.0741	191.7582	250.1742	34.1573	1.0281	NaN	3.9238	1.5357	10.8251	18.9849	9.0113	0.0000	0.0000	240.7767	244.2748	NaN	NaN	NaN	36.9067	2.9626	14.5293	0.0751	7.0870	12.1831	0.6451	6.4568	2.1538	2.9667	9.3046	0.1096	0.0078	0.0026	7.116	0.7942	411.136	74.654	0.1832	16.16	0.85	0.0857	7.1619	0.3752	23.0713	3.9306	1.1386	1.5021	0.3718	0.1233	24.7524	267.064	0.9032	1.10	0.6219	0.4122	0.2562	0.4119	68.8489	535.0245	2.0293	11.21	0.1882	4.0923	0.0640	2.0952	9.2721	0.0584	0.0484	0.0148	82.8602	0.4958	0.0157	0.0039	3.1745	0.0584	0.0484	0.0148	82.8602	1
3	2008-07-19 14:43:00	2988.72	2479.90	2199.0333	909.7926	1.3204	100.0	104.2367	0.1217	1.4882	-0.0124	-0.0033	0.9629	201.8482	9.6052	422.2894	9.6924	0.9687	192.1557	12.4782	1.4011	-5468.25	2648.25	-4515.00	-1657.25	1.3137	2.0038	7.3145	62.9333	2.6444	0.2071	3.3813	84.9105	8.6789	50.5100	64.1125	49.4900	65.1951	86.6867	117.0442	76.90	1.279	70.0	363.0273	9.9305	131.8027	733.8778	1.3027	142.5427	1.0	637.3727	189.9079	4.486	4.748	2936.0	0.9139	0.9447	4.5873	24.3791	361.4582	10.2112	116.1818	13.5597	15.6209	23.4736	710.4043	0.9761	147.6545	1.0	625.2945	70.2289	160.3210	464.9735	-0.0555	-0.0461	-0.0400	0.0400	0.0676	-0.1051	0.0028	0.0277	7.5925	0.1302	NaN	2.4004	0.9904	1752.0968	0.1958	8205.7000	0.0697	-0.0003	-0.0021	-0.0001	0.0002	0.0411	0.0177	-0.0195	-0.0002	0.0000	-0.0699	-0.0059	0.0003	0.0003	0.0021	-0.0483	-0.1180	NaN	NaN	NaN	0.4647	0.9564	709.0867	0.9906	58.6635	0.6016	0.9761	6.4935	15.55	3.132	15.61	15.59	1.3660	2.480	0.5176	3.119	0.2838	0.7244	0.9961	2.3802	980.4510	41.1025	127.0	118.0	123.7	47.8000	162.4320	0.1915	5.51	0.0030	0.1140	0.0393	0.0613	0.0190	13.2651	9.073	15.241	1.3029	0.0150	11.9738	0.35	0.0699	NaN	NaN	859.0	355.0	3433.0	3004.0	0.068	0.108	0.100	1.7	0.9	0.086	0.241	0.9386	0.0356	0.2618	0.4391	0.2618	0.8567	0.2452	0.390	16.22	0.693	14.67	22.562	0.1786	5.69	18.20	52.571	0.139	5.92	23.60	1.264	10.63	13.56	5.92	11.382	24.320	0.3458	9.56	21.97	104.950	0.1248	0.0463	0.1223	0.0354	0.0708	0.0754	0.0643	0.0932	5.5398	0.0023	NaN	0.0764	0.0015	152.0885	0.0573	820.3999	0.0152	0.0149	0.0067	0.0040	NaN	NaN	NaN	0.0191	0.0234	94.0954	0.0010	3.2119	0.0406	0.0072	0.4212	0.0513	31.830	3.1959	33.8960	37.8477	44.3906	16.9347	50.3631	0.0581	2.1775	0.0007	0.0417	0.0115	0.0172	0.0063	4.2154	2.8960	4.0526	0.3882	0.0049	3.9403	0.0916	0.0245	NaN	NaN	415.5048	157.0889	1572.6896	1377.4276	0.0285	0.0445	0.0465	0.6305	0.3046	0.0286	0.0824	0.3483	0.0128	0.1004	0.1701	0.1004	0.3465	0.0973	0.1675	5.4440	0.2004	4.1900	6.3329	0.0479	1.7339	4.9660	15.7375	0.0243	1.7317	6.6262	0.3512	3.2699	9.4020	1.7317	3.0672	6.6839	0.0928	3.0229	6.3292	29.0339	8.4026	4.8851	0.0407	0.0198	0.0531	0.0167	0.0224	0.0422	0.0273	0.0484	1.8222	0.0006	NaN	0.0252	0.0004	45.7058	0.0188	309.8492	0.0046	0.0049	0.0028	0.0034	0.0024	0.0014	NaN	NaN	NaN	0.0038	0.0068	32.4228	0.0003	1.1135	0.0132	0.0023	0.1348	0.0155	13.3972	1.1907	5.6363	3.9482	4.9881	2.1737	17.8537	14.5054	5.2860	2.4643	7.6602	317.7362	0.0000	1.9689	6.5718	94.4594	3.6091	13.4420	1.5441	6.2313	2.8049	4.9898	15.7089	13.4051	76.0354	181.2641	5.1760	5.3899	1.3671	2.7013	34.0336	41.5236	7.1274	1.1054	0.4097	0.5183	0.6849	0.5290	1.3141	0.2829	0.3332	4.4680	6.9785	11.1303	3.0744	13.7105	3.9918	2.8555	27.6824	3.0301	24.2831	6.5291	12.3786	9.1494	100.0021	37.8979	48.4887	3.4234	35.4323	6.4746	3.5135	149.4399	225.0169	100.4883	305.7500	88.5553	104.6660	71.7583	0.0000	336.7660	72.9635	1.7670	NaN	3.1817	0.1488	8.6804	29.2542	9.9979	0.0000	711.6418	113.5593	0.0000	NaN	NaN	NaN	4.1200	2.4416	13.2699	0.0977	5.4751	6.7553	0.7404	6.4865	2.1565	3.2465	7.7754	0.1096	0.0078	0.0026	7.116	1.1650	372.822	72.442	1.8804	131.68	39.33	0.6812	56.9303	17.4781	161.4081	35.3198	54.2917	1.1613	0.7288	0.2710	62.7572	268.228	0.6511	7.32	0.1630	3.5611	0.0670	2.7290	25.0363	530.5682	2.0253	9.33	0.1738	2.8971	0.0525	1.7585	8.5831	0.0202	0.0149	0.0044	73.8432	0.4990	0.0103	0.0025	2.0544	0.0202	0.0149	0.0044	73.8432	-1
4	2008-07-19 15:22:00	3032.24	2502.87	2233.3667	1326.5200	1.5334	100.0	100.3967	0.1235	1.5031	-0.0031	-0.0072	0.9569	201.9424	10.5661	420.5925	10.3387	0.9735	191.6037	12.4735	1.3888	-5476.25	2635.25	-3987.50	117.00	1.2887	1.9912	7.2748	62.8333	3.1556	0.2696	3.2728	86.3269	8.7677	50.2480	64.1511	49.7520	66.1542	86.1468	121.4364	76.39	2.209	70.0	353.3400	10.4091	176.3136	789.7523	1.0341	138.0882	1.0	667.7418	233.5491	4.624	4.894	2865.0	0.9298	0.9449	4.6414	-12.2945	355.0809	9.7948	144.0191	21.9782	32.2945	44.1498	745.6025	0.9256	146.6636	1.0	645.7636	65.8417	NaN	NaN	-0.0534	0.0183	-0.0167	-0.0449	0.0034	-0.0178	-0.0123	-0.0048	7.5017	0.1342	NaN	2.4530	0.9902	1828.3846	0.1829	9014.4600	0.0448	-0.0077	-0.0001	-0.0001	-0.0001	0.2189	-0.6704	-0.0167	0.0004	-0.0003	0.0696	-0.0045	0.0002	0.0078	0.0000	-0.0799	-0.2038	NaN	NaN	NaN	NaN	0.9424	796.5950	0.9908	58.3858	0.5913	0.9628	6.3551	15.75	3.148	15.73	15.71	0.9460	3.027	0.5328	3.299	-0.5677	0.7780	1.0010	2.3715	993.1274	38.1448	119.0	143.2	123.1	48.8000	296.3030	0.3744	3.64	0.0041	0.0634	0.0451	0.0623	0.0240	14.2354	9.005	12.506	0.4434	0.0126	13.9047	0.43	0.0538	NaN	NaN	699.0	283.0	1747.0	1443.0	0.147	0.040	0.113	3.9	0.8	0.101	0.499	0.5760	0.0631	0.3053	0.5830	0.3053	0.8285	0.1308	0.922	15.24	0.282	10.85	37.715	0.1189	3.98	25.54	72.149	0.250	5.52	15.76	0.519	10.71	19.77	5.52	8.446	33.832	0.3951	9.09	19.77	92.307	0.0915	0.0506	0.0769	0.1079	0.0797	0.1047	0.0924	0.1015	4.1338	0.0030	NaN	0.0802	0.0004	69.1510	0.1970	1406.4004	0.0227	0.0272	0.0067	0.0031	NaN	NaN	NaN	NaN	0.0240	149.2172	0.0006	2.5775	0.0177	0.0214	0.4051	0.0488	19.862	3.6163	34.1250	55.9626	53.0876	17.4864	88.7672	0.1092	1.0929	0.0013	0.0257	0.0116	0.0163	0.0080	4.4239	3.2376	3.6536	0.1293	0.0040	4.3474	0.1275	0.0181	NaN	NaN	319.1252	128.0296	799.5884	628.3083	0.0755	0.0181	0.0476	1.3500	0.2698	0.0320	0.1541	0.2155	0.0310	0.1354	0.2194	0.1354	0.3072	0.0582	0.3574	4.8956	0.0766	2.9130	11.0583	0.0327	1.1229	7.3296	23.1160	0.0822	1.6216	4.7279	0.1773	3.1550	9.7777	1.6216	2.5923	10.5352	0.1301	3.0939	6.3767	32.0537	NaN	NaN	0.0246	0.0221	0.0329	0.0522	0.0256	0.0545	0.0476	0.0463	1.5530	0.0010	NaN	0.0286	0.0001	21.0312	0.0573	494.7368	0.0063	0.0077	0.0052	0.0027	0.0025	0.0012	NaN	NaN	NaN	NaN	0.0089	57.2692	0.0002	0.8495	0.0065	0.0077	0.1356	0.0165	7.1493	1.1704	5.3823	4.7226	4.9184	2.1850	22.3369	24.4142	3.6256	3.3208	4.2178	0.0000	866.0295	2.5046	7.0492	85.2255	2.9734	4.2892	1.2943	7.2570	3.4473	3.8754	12.7642	10.7390	43.8119	0.0000	11.4064	2.0088	1.5533	6.2069	25.3521	37.4691	15.2470	0.6672	0.7198	0.6076	0.9088	0.6136	1.2524	0.1518	0.7592	4.3131	2.7092	6.1538	4.7756	11.4945	2.8822	3.8248	30.8924	5.3863	44.8980	4.4384	5.2987	7.4365	89.9529	17.0927	19.1303	4.5375	42.6838	6.1979	3.0615	140.1953	171.4486	276.8810	461.8619	240.1781	0.0000	587.3773	748.1781	0.0000	55.1057	2.2358	NaN	3.2712	0.0372	3.7821	107.6905	15.6016	293.1396	0.0000	148.0663	0.0000	NaN	NaN	NaN	NaN	2.5512	18.7319	0.0616	4.4146	2.9954	2.2181	6.3745	2.0579	1.9999	9.4805	0.1096	0.0078	0.0026	7.116	1.4636	399.914	79.156	1.0388	19.63	1.98	0.4287	9.7608	0.8311	70.9706	4.9086	2.5014	0.9778	0.2156	0.0461	22.0500	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	532.0155	2.0275	8.83	0.2224	3.1776	0.0706	1.6597	10.9698	NaN	NaN	NaN	NaN	0.4800	0.4766	0.1045	99.3032	0.0202	0.0149	0.0044	73.8432	-1

	count	mean	std	min	25%	50%	75%	max
0	1561.0	3014.452896	73.621787	2743.2400	2966.260000	3011.49000	3056.650000	3356.3500
1	1560.0	2495.850231	80.407705	2158.7500	2452.247500	2499.40500	2538.822500	2846.4400
2	1553.0	2200.547318	29.513152	2060.6600	2181.044400	2201.06670	2218.055500	2315.2667
3	1553.0	1396.376627	441.691640	0.0000	1081.875800	1285.21440	1591.223500	3715.0417
4	1553.0	4.197013	56.355540	0.6815	1.017700	1.31680	1.525700	1114.5366
5	1553.0	100.000000	0.000000	100.0000	100.000000	100.00000	100.000000	100.0000
6	1553.0	101.112908	6.237214	82.1311	97.920000	101.51220	104.586700	129.2522
7	1558.0	0.121822	0.008961	0.0000	0.121100	0.12240	0.123800	0.1286
8	1565.0	1.462862	0.073897	1.1910	1.411200	1.46160	1.516900	1.6564
9	1565.0	-0.000841	0.015116	-0.0534	-0.010800	-0.00130	0.008400	0.0749
10	1565.0	0.000146	0.009302	-0.0349	-0.005600	0.00040	0.005900	0.0530
11	1565.0	0.964353	0.012452	0.6554	0.958100	0.96580	0.971300	0.9848
12	1565.0	199.956809	3.257276	182.0940	198.130700	199.53560	202.007100	272.0451
13	1564.0	0.000000	0.000000	0.0000	0.000000	0.00000	0.000000	0.0000
14	1564.0	9.005371	2.796596	2.2493	7.094875	8.96700	10.861875	19.5465
15	1564.0	413.086035	17.221095	333.4486	406.127400	412.21910	419.089275	824.9271
16	1564.0	9.907603	2.403867	4.4696	9.567625	9.85175	10.128175	102.8677
17	1564.0	0.971444	0.012062	0.5794	0.968200	0.97260	0.976800	0.9848
18	1564.0	190.047354	2.781041	169.1774	188.299825	189.66420	192.189375	215.5977
19	1557.0	12.481034	0.217965	9.8773	12.460000	12.49960	12.547100	12.9898
20	1567.0	1.405054	0.016737	1.1797	1.396500	1.40600	1.415000	1.4534
21	1565.0	-5618.393610	626.822178	-7150.2500	-5933.250000	-5523.25000	-5356.250000	0.0000
22	1565.0	2699.378435	295.498535	0.0000	2578.000000	2664.00000	2841.750000	3656.2500
23	1565.0	-3806.299734	1380.162148	-9986.7500	-4371.750000	-3820.75000	-3352.750000	2363.0000
24	1565.0	-298.598136	2902.690117	-14804.5000	-1476.000000	-78.75000	1377.250000	14106.0000
25	1565.0	1.203845	0.177600	0.0000	1.094800	1.28300	1.304300	1.3828
26	1565.0	1.938477	0.189495	0.0000	1.906500	1.98650	2.003200	2.0528
27	1565.0	6.638628	1.244249	0.0000	5.263700	7.26470	7.329700	7.6588
28	1565.0	69.499532	3.461181	59.4000	67.377800	69.15560	72.266700	77.9000
29	1565.0	2.366197	0.408694	0.6667	2.088900	2.37780	2.655600	3.5111
30	1565.0	0.184159	0.032944	0.0341	0.161700	0.18670	0.207100	0.2851
31	1565.0	3.673189	0.535322	2.0698	3.362700	3.43100	3.531300	4.8044
32	1566.0	85.337469	2.026549	83.1829	84.490500	85.13545	85.741900	105.6038
33	1566.0	8.960279	1.344456	7.6032	8.580000	8.76980	9.060600	23.3453
34	1566.0	50.582639	1.182618	49.8348	50.252350	50.39640	50.578800	59.7711
35	1566.0	64.555787	2.574749	63.6774	64.024800	64.16580	64.344700	94.2641
36	1566.0	49.417370	1.182619	40.2289	49.421200	49.60360	49.747650	50.1652
37	1566.0	66.221274	0.304141	64.9193	66.040650	66.23180	66.343275	67.9586
38	1566.0	86.836577	0.446756	84.7327	86.578300	86.82070	87.002400	88.4188
39	1566.0	118.679554	1.807221	111.7128	118.015600	118.39930	118.939600	133.3898